Reviewerly/exHarmony huggingface dataset:
The state of training of art education specialists in the universities of the European Union and the USA for the use of music therapy in professional activities

The goal of modern university education in Europe and the USA is training, upbringing, harmonious development and socialization of a highly moral, spiritually developed personality, which takes place with the use of health-preserving technologies. This goal acquires great importance precisely in the conditions of the educational space, since it covers a significant part of the population, which is characterized by differences in cultural level, material status, and age. The number of students with special needs is increasing in university education. The use of music therapy in the modern university space contributes to solving the identified problems, for which it is necessary to implement: highly qualified training of personnel in music therapy and educational projects and programs in universities in Europe and the USA. During the 20th and 21st centuries in Europe and the USA, music therapy began to be intensively introduced into university education to activate educational and intellectual activity, model a health-saving educational environment, meet the special needs of student youth in inclusive education, and create a STEAM learning environment. To date, Ukrainian scientists have systematized only certain aspects of scientific knowledge about music therapy, which is introduced into the content of modern university education in Europe and the USA. In this way, the leading international experience on the training of specialists in the artistic field to apply music therapy in professional activities has not been summarized, the system of measures to attract students to these classes has not been substantiated, and data on the specifics of the content of classes with music therapy tools have not been systematized. The consequence of using music therapy in the university environment should be the expression of feelings and positive changes in the behavior of subjects of education during communication with others and reflection. Music therapy solves communication issues, improves motivation, attention, behavior, improves memory and the ability to learn, affects the emotional sphere of the individual.
A Review of Breast Cancer Classification and Detection Techniques

Cancer is a very serious disease. Deadly and very hard if you lived with. Cancer is caused when abnormal changes happens on cells that are out of control.Cancer cells often form a lump or mass called a tumor and are named after the part of the body in which they originated. When Happens at breast organ it is called Breas Cancer. Deadly as any other type of cancer. Also as any other type, it has a life cycle consist of stages. usually does not produce any pain in the early stages in which it is easy to treat on. Cancer main problem is when to find out that the patient had cancer cell inside his body. Early stages always it better timing to start treating the cancer tumors. All old reaserchers and doctors always tried to improve the medical processe in which we try to find cancer cells also the other medical checkups to help us detect or even predict that this person had or will have a cancer disease in the future. CAD systems and Medical Screening was the main ways to think about. Medical Images processing technologies and finding best features of cells was the right way and using CNN, Deep CNN, Convoltion Matricese, SVM Technique and many of Machine Learning algorithms used before, the main challenge was to make it easy and also more accurate to complete these tests. Many improvements made at this track of work. The results always was More and more of accuracy improvements and time reduction and resources usage improvements too.
Pengembangan Pembelajaran Guru Sekolah Menengah Atas (SMA) Melalui Pemanfaatan Multimedia yang Menarik dan Efektif Di SMA Entrepreneurship Bi’rul Ulum Serang

To improve the quality of learning in the Bi'rul Ulum Entrepreneurship High School environment, the use of multimedia in education has been the main focus of this study. The study also aims to explore and evaluate how the use of multimedia affects the learning of subject teachers, as well as how effective the use of multimedia is in increasing student engagement and understanding of the material. Involving a large number of students and teachers from various levels of education, qualitative and quantitative research methods were applied. Data was collected through interviews, observations, questionnaires, and evaluations of student learning outcomes before and after the implementation of multimedia. The results of the study showed that teachers and students responded well to the use of multimedia in learning. Teachers reported that students were more engaged and easier to deliver material, and students showed that they were more interested and understood what they learned after using media. In addition, the evaluation of student learning outcomes showed that the use of media improved student academic achievement. These results suggest that teachers should receive more in-depth training, create materials that are more integrated with technology, and create school policies that support the use of multimedia in learning. The study enhances our understanding of the role of multimedia in teacher learning development and shows its great potential to be utilised to improve the quality of education in the future. Keywords: multimedia in education, teacher learning development, technology in learning, utilisation of multimedia in teaching, effectiveness of multimedia in education
Name from the Past: Nikolai Khristianovich Vessel in the Service of Russian Education

State policy in the field of education, as a rule, is dictated by the demands of the time, any changes in this extremely important area are not immediately noticeable. The results of innovations carry a delayed action and, appearing gradually, sometimes lead to irreparable consequences. Structuring the education system should be one of the state’s priorities. Throughout history, the education system of our country has repeatedly undergone significant transformations, in this regard, it is interesting to turn to the historical experience in the educational sphere accumulated by previous generations. Today, the interest of researchers in the scientific and pedagogical heritage of Russian public figures has significantly increased. The name of Nikolai Khristianovich Vessel, long forgotten, belongs to that plethora of brilliant Russian teachers of the second half of the XIX century, whose works are in demand today. The purpose of the article is to introduce into scientific circulation new biographical data found on the pages of his unpublished and little-known works and to present N.Kh. Vessel not only as an expert in the field of pedagogy, but also as a person distinguished by broad multifaceted interests, in particular in the field of finance.
Research on comprehensive service quality evaluation of smart expressway service area based on combinatorial empowerment cloud model

In order to comprehensively evaluate the service quality of smart expressway service area, we established an evaluation index system for the service quality of smart expressway service areas and an evaluation method to scientifically evaluate the service quality . Based on the subjective weighting of G1 method and the objective weighting of COWA operator, the game theory combination weighting method with the goal of minimizing the deviation is used for weight weighting. The comprehensive evaluation cloud map was obtained to determine the level of smartization of expressway service areas. As case study, a smart service area between Xingxing Gorge and Khorgos port section of the G30 Express connecting Lianhuo Expressway was selected for evaluation. The results showed that the project was between the smart development level (D3 level) and the smart cooperation level (D2 level), which was consistent with the actual situation of the project.
Histopathologic Cancer Detection with Hybrid Deep Learning model

Histopathological examination, as the "gold standard" recognized by the medical community, can easily reach the resolution of microns and is a qualitative examination. With the rapid development of computer hardware and software, deep learning has been triggered in the computer-aided diagnosis of medical images. To create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans, we proposed a hybrid deep learning model which combining Resnet and Densenet We combined Resnet and Densenet to extract image features. We introduced some related work about the histopathological examination. In the experiment, we compared our model with other models, the elevation metrics is accuracy and Auc-Roc score. From the definition, the higher Auc Roc Score and accuracy are, the better performance the model will gain. On the modified version of the PatchCamelyon (PCam) benchmark dataset, Our model achieved the highest AUC score (0.971) and highest accuracy (0.982) on the test set.
LibGuides: Architecture: Journals and databases

Library information for Architecture students Library and Learning Resources provide access to an extensive range of electronic information sources.
If Cognitive Science is Multidisciplinary, Which are the Disciplines? Cognitive Science as Three Methodological Cultures

If Cognitive Science is Multidisciplinary, Which are the Disciplines? Cognitive Science as Three Methodological Cultures
Segmented In-Advance Data Analytics for Fast Scientific Discovery

Scientific discovery usually involves data generation, data preprocessing, data storage and data analysis. As the data volume exceeds a few terabytes (TB) in a single simulation run, the data movement, which happens during each cycle of the scientific discovery, continues to be the bottleneck in most scientific big data applications. A lot of research works have been conducted on reducing the data movement. Among the existing efforts and based on our previous research, reusing the analysis results shows a significant potential in optimizing the data movement between analysis operations. In this work, we propose the Segmented In-Advance (SIA) data analytics approach for optimizing the data movement and we also provide a cloud-based elastic distributed in-memory database to manage the intermediate analysis results. The fundamental idea of this Segmented In-Advance approach is to analyze the history operations and to predict the future interesting analytics operations. The predicted analysis operation is in-advance performed on the finer segmented dataset and the segmented results are distributed in an in-memory key-value store for future reuse. The evaluation shows that the segmented in-advance data analytics approach achieves 1.2X-6.1X speedup. The evaluation also shows a good scalability of the in-memory distributed data store. The proposed Segmented In-Advance data analytics approach is a promising data movement reduction solution for scientific big data applications and fast scientific discovery.
Rolling path planning of mobile robot based on automatic diffluence ant algorithm

This paper proposes a new method of robot path rolling planning based on automatic shunt ant algorithm. When a node is selected by multiple ants, later ants choose other paths to realize automatic shunting, thereby expanding the search range, enhancing the search diversity, and helping to obtain the optimal solution. The overall idea of the algorithm in this paper is to map the target point near the inner boundary of the robot's field of view, and use the new algorithm to plan the local optimal path of the robot, and the robot will move forward according to this local path. The robot repeats the process every time it advances, and reaches the end safely along a globally optimized path. Simulation experiments show that even in a complex unknown static environment, the algorithm in this paper can also be used to plan a global optimization path.
User Responses to the Learning Demands of Consumer Products

Designers are typically male, under 35 years old and unimpaired. Users can be of any age and currently over 15% will have some form of impairment. As a result a vast array of consumer products suit youthful males and in many cases exclude other demographics (e.g. Keates and Clarkson, 2004). In studying the way a range of users learn how to use new products, key cognitive difficulties are revealed and linked back to the areas of the product causing the problems. The trials were structured so each user had to complete a specific set of tasks and were consistent across the user spectrum. The tasks set aimed to represent both everyday usage and less familiar functions. Whilst the knowledge gained could provide designers with valuable guidelines for the specific products examined, a more general abstraction provides knowledge of the pitfalls to avoid in the design of other product families.
Prosodic and pragmatic properties of affirmative words in European Portuguese

This paper investigates the correlation between the prosodic behavior and pragmatic functions of affirmative words from a spoken dialogue corpus collected for European Portuguese (EP). The analysis of 460 affirmative answers shows that affirmative words expressing agreement occur more often than those interpreted as auto positive and confirm. Low/falling and high/rising nuclear contours correlate with affirmative words with different pragmatic functions: agreement and confirm are commonly uttered with (H+)L* L%, whereas auto positive is commonly uttered with L*+H / (L+)H* H%. The analysis of acoustic/prosodic cues reveals that the three functions of the affirmative word sim ‘yes’ have significant differences (p<.001 and p<.05). A pitch concord effect is also found between functions, although expressed in different degrees.
QEBA: Query-Efficient Boundary-Based Blackbox Attack

Machine learning (ML), especially deep neural networks (DNNs) have been widely used in various applications, including several safety-critical ones (e.g. autonomous driving). As a result, recent research about adversarial examples has raised great concerns. Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction. While several whitebox attacks have demonstrated their effectiveness, which assume that the attackers have full access to the machine learning models; blackbox attacks are more realistic in practice. In this paper, we propose a Query-Efficient Boundary-based blackbox Attack (QEBA) based only on model’s final prediction labels. We theoretically show why previous boundary-based attack with gradient estimation on the whole gradient space is not efficient in terms of query numbers, and provide optimality analysis for our dimension reduction-based gradient estimation. On the other hand, we conducted extensive experiments on ImageNet and CelebA datasets to evaluate QEBA. We show that compared with the state-of-the-art blackbox attacks, QEBA is able to use a smaller number of queries to achieve a lower magnitude of perturbation with 100% attack success rate. We also show case studies of attacks on real-world APIs including MEGVII Face++ and Microsoft Azure.
An ADMM Approach to Masked Signal Decomposition Using Subspace Representation

Signal decomposition is a classical problem in signal processing, which aims to separate an observed signal into two or more components, each with its own property. Usually, each component is described by its own subspace or dictionary. Extensive research has been done for the case where the components are additive, but in real-world applications, the components are often non-additive. For example, an image may consist of a foreground object overlaid on a background, where each pixel either belongs to the foreground or the background. In such a situation, to separate signal components, we need to find a binary mask which shows the location of each component. Therefore, it requires solving a binary optimization problem. Since most of the binary optimization problems are intractable, we relax this problem to the approximated continuous problem and solve it by alternating optimization technique. We show the application of the proposed algorithm for three applications: separation of text from a background in images, separation of moving objects from a background undergoing global camera motion in videos, and separation of sinusoidal and spike components in 1-D signals. We demonstrate in each case that considering the non-additive nature of the problem can lead to a significant improvement.
Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning

Deep convolutional neural networks (CNN) have recently achieved superior performance at the task of medical image segmentation compared to classic models. However, training a generalizable CNN requires a large amount of training data, which is difficult, expensive, and time-consuming to obtain in medical settings. Active Learning (AL) algorithms can facilitate training CNN models by proposing a small number of the most informative data samples to be annotated to achieve a rapid increase in performance. We proposed a new active learning method based on Fisher information (FI) for CNNs for the first time. Using efficient backpropagation methods for computing gradients together with a novel low-dimensional approximation of FI enabled us to compute FI for CNNs with a large number of parameters. We evaluated the proposed method for brain extraction with a patch-wise segmentation CNN model in two different learning scenarios: universal active learning and active semi-automatic segmentation. In both scenarios, an initial model was obtained using labeled training subjects of a source data set and the goal was to annotate a small subset of new samples to build a model that performs well on the target subject(s). The target data sets included images that differed from the source data by either age group (e.g. newborns with different image contrast) or underlying pathology that was not available in the source data. In comparison to several recently proposed AL methods and brain extraction baselines, the results showed that FI-based AL outperformed the competing methods in improving the performance of the model after labeling a very small portion of target data set (<; 0.25%).
LAMBERT: Layout-Aware Language Modeling for Information Extraction

We introduce a simple new approach to the problem of understanding documents where non-trivial layout influences the local semantics. To this end, we modify the Transformer encoder architecture in a way that allows it to use layout features obtained from an OCR system, without the need to re-learn language semantics from scratch. We only augment the input of the model with the coordinates of token bounding boxes, avoiding, in this way, the use of raw images. This leads to a layout-aware language model which can then be fine-tuned on downstream tasks.The model is evaluated on an end-to-end information extraction task using four publicly available datasets: Kleister NDA, Kleister Charity, SROIE and CORD. We show that our model achieves superior performance on datasets consisting of visually rich documents, while also outperforming the baseline RoBERTa on documents with flat layout (NDA \(F_{1}\) increase from 78.50 to 80.42). Our solution ranked first on the public leaderboard for the Key Information Extraction from the SROIE dataset, improving the SOTA \(F_{1}\)-score from 97.81 to 98.17.
A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants

Solar power generation (SPG) is essentially dependent on spatial and meteorological characteristics which makes the planning and operation of power systems difficult. To promote the integration of solar power into electric power grid, accurate prediction of geographically distributed SPG is needed. In this paper, we present a combined method for day-ahead SPG prediction of multi-region photovoltaic (PV) plants. First, automatic machine learning (AML) is applied to generate the most suitable ensemble prediction model with optimal parameters and then an improved genetic algorithm (GA) is implemented which processes the candidate features by assigning appropriate operators. To achieve more accurate forecast results as well as mine the interpretable relationship between SPG and related weather or PV system factors, the SPG physical model is taken into account. The method performance is evaluated by the real SPG data along with meteorological variables of multi-region PV plants in Hokkaido from 2016 to 2018. Results indicate that the combined method provides acceptable accuracy and outperforms several baselines and other methods used for comparison.
Dynamical Phase Transitions in Quantum Reservoir Computing

Closed quantum systems exhibit different dynamical regimes, like many-body localization or thermalization, which determine the mechanisms of spread and processing of information. Here we address the impact of these dynamical phases in quantum reservoir computing, an unconventional computing paradigm recently extended into the quantum regime that exploits dynamical systems to solve nonlinear and temporal tasks. We establish that the thermal phase is naturally adapted to the requirements of quantum reservoir computing and report an increased performance at the thermalization transition for the studied tasks. Uncovering the underlying physical mechanisms behind optimal information processing capabilities of spin networks is essential for future experimental implementations and provides a new perspective on dynamical phases.
Efficient semiquantum key distribution based on single photons in both polarization and spatial-mode degrees of freedom

In this paper, we propose an efficient semiquantum key distribution (SQKD) protocol which is based on single photons in both polarization and spatial-mode degrees of freedom. This protocol is feasible for a quantum communicant distributing a random private key to a classical communicant. This protocol need not require the classical communicant to use any quantum memory or unitary operation equipment. We validate the complete robustness of the transmissions of single photons between two communicants. It turns out that during these transmissions, if Eve wants not to be detected by two communicants, she will obtain nothing useful about the final shared key bits. Compared with Boyer et al.’s famous pioneering SQKD protocol (Phys Rev Lett 99:140501, 2007), this protocol has double quantum communication capacity, as one single photon with two degrees of freedom for generating the key bits can carry two private bits; and this protocol has higher quantum communication efficiency, as it consumes less qubits for establishing a private key of the same length. Compared with the only existing SQKD protocol with single photons in two degrees of freedom (Int J Theoret Phys 59:2807, 2020), this protocol has higher quantum communication efficiency.
Learning Discrete Structures for Graph Neural Networks

Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between data points. Unfortunately, GNNs can only be used when such a graph-structure is available. In practice, however, real-world graphs are often noisy and incomplete or might not be available at all. With this work, we propose to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph. This allows one to apply GCNs not only in scenarios where the given graph is incomplete or corrupted but also in those where a graph is not available. We conduct a series of experiments that analyze the behavior of the proposed method and demonstrate that it outperforms related methods by a significant margin.