Reviewerly/exHarmony huggingface dataset:
A teacher-student framework to distill future trajectories

By learning to predict trajectories of dynamical systems, model-based methods can make extensive use of all observations from past experience. However, due to partial observability, stochasticity, compounding errors, and irrelevant dynamics, training to predict observations explicitly often results in poor models. Model-free techniques try to side-step the problem by learning to predict values directly. While breaking the explicit dependency on future observations can result in strong performance, this usually comes at the cost of low sample efficiency, as the abundant information about the dynamics contained in future observations goes unused. Here we take a step back from both approaches: Instead of hand-designing how trajectories should be incorporated, a teacher network learns to interpret the trajectories and to provide target activations which guide a student model that can only observe the present. The teacher is trained with meta-gradients to maximize the student's performance on a validation set. We show that our approach performs well on tasks that are difficult for model-free and model-based methods, and we study the role of every component through ablation studies.
Aspect Multiple Product Lines

Software product line (SPL) approach has known a great attention in the software field due to the advantages it brings to software-development. In some large fields having a single SPL is no longer sufficient to manage variability. Several separated SPLs rise then resulting in what is known by multiple product lines (MPL). However, the adoption of an MPL results in several challenges namely: managing reuse between SPLs, MPL model complexity and distributed-derivation. In this paper, we present a new methodology for MPLs engineering called AMPL. Our approach helps managing reuse across MPLs basing on two key concepts: separation of concerns and partial derivation. AMPL approach is validated in the context of an e-government MPL and its various steps are illustrated along the paper. The results show that our approach helps systemising reuse within MPLs and structuring the MPL model. Moreover, SPLs are integrated early in the development process thus avoiding the distributed-derivation challenges.
Found Sounds

This recipe encourages learners to develop their critical and analytic listening skills by seeking and recording sounds in their environment as a basis for music composition. The use of unorthodox (i.e. not inherently musical) sounds in a compositional context develops critical listening, creative use of recorded material, and encourages the exploration of the sonic and functional possibilities afforded by DAW technologies in working with and manipulating audio as a basis for composition. Designed for first-year university students studying popular music composition, learners may find this activity engaging and enriching as it encourages outside-of-the-box thinking and attuning to a more vast world of sound and music.
Trend Analysis of Malaria Prevalence in East Wollega Zone, Oromia Regional State, Western Ethiopia, 2020: A Retrospective Study

Background: Malaria, a common and life-threatening disease in many tropical and subtropical areas, caused by infection of red blood cells with protozoan parasites of the genus plasmodium inoculated into the human host by a feeding female anopheline mosquito. Malaria is a major public health problem in Ethiopia and has been consistently reported as one of the three leading top causes of morbidity and mortality. In East Wollega Zone there is lack of empirical evidences on the level of malaria prevalence.Material and Methods: A retrospective study was carried out to determine the two year (July 2018 to June 2020) malaria prevalence based on district health information system version two (dhis2) database reports. All malaria cases reported in the specified periods were carefully reviewed by using questionnaire and analysed. Data were entered, processed and analysed into Microsoft Excel 2010 and then imported into Statistical Package for the Social Sciences (SPSS) version 24. Different graphs and tables were used to present trends of malaria cases and total population.Results: Between July 2018 to June 2018, a total of 125,917 suspected malaria cases examined from all districts of East Wollega Zone and 26,679(21.2%) confirmed malaria cases were reported. Malaria was reported in both sexes and all age groups, but males (14802, 55.5%), and age groups ≥ 15years (15963, 60%) were more affected. The highest peak of malaria cases was reported during autumn season (September, October, and November) followed by spring season (March, April, and May).Conclusions: Male and those age groups ≥ 15years were more affected than the others. The highest peak malaria prevalence was appeared from September to December (autumn season). Therefore, proper planning, implementation and monitor of malaria prevention and control activities should be strengthened at all levels.
무능력함을 표시하는 한국인 감사화행 전략의 진실성 연구: 남녀, 연령, 학력에 따른 차이 여부

This study examined differences in gender, age and educational status to find out the veracity of incompetency strategy in gratitude speech act. In daily life, Koreans often express incompetency in the context where their gratitude has to be expressed. It could mean that incompetency strategy is used to express continuous relationship, particular types of thoughts and value criterion in Korean culture. To prove this point, a survey by questionnaire which consists of psychologically grateful situations was conducted. The findings of the study are as follows. First, there were no significant differences in gender, age and educational status in relation to the veracity of the incompetency strategy in gratitude speech act used by each group. Second, incompetency strategy in gratitude speech act was a general trend in the Korean speech community, especially in psychological situations.
Decentralized Planning for Active Information Gathering with Probabilistic Model

Motion strategies for multiple robots that actively acquire information in a dynamic environment have been widely studied. However, the existing active information gathering algorithms are restricted by the assumption of linear target dynamics or completely known models. In this study, we formulate the active information gathering problem with the belief distribution of the desired target information with unknown underlying dynamics. The reward function is derived based on the mutual information of the measurement and belief distribution, and it can be efficiently computed under the Gaussian assumption on the belief distribution. Moreover, a decentralized path planner is designed to maximize the reward function, which scales well in terms of both the numbers of agents and targets. We apply the proposed planner to an active target tracking scenario and validate the performance and scalability through a numerical simulation.
Benchmarking General-Purpose Neural Networks for Real-Time Pedestrian Detection

This paper presents the main steps and results on automotive applications aiming to detect pedestrians by means of object detection and recognition neural networks. The used training and testing datasets take into account the influence of different factors, such as environmental conditions and video-image acquisition characteristics, using a custom training and testing dataset, and critically evaluate the performance and capabilities of general-purpose neural networks in detecting pedestrians from usual images provided by a video camera.Following this goal, an analysis points out what differences occur in the final classification output of these neuronal networks using the same dataset for training while testing on real-life scenarios. In the end a general-purpose neural network which provides the best results in pedestrian detection (providing 2D bounding boxes) is proposed and its performances are discussed.
Job Satisfaction And Employee Work Performance Among Academic Staff: A Case of Shah Abdul Latif University, Khairpur Mirs

To identify the implications of Job satisfaction on Job performance of academic staffs Shah Abdul Latif University Khairpur Mirs. A questionnaire survey is used in this study. There were 85 participants of the Academic staff of SALU Khairpur Mirs. The questionnaire is based on 25 items that measured the effect of academic staffs satisfaction level & level of performance being affected. Job satisfaction is an independent factor and job performance is the dependent factor. Inferences of job satisfaction on job performance are observed by five variables of job satisfaction namely: Work, Recognition, Achievement, Responsibility, and Autonomy. Cronbachs alpha is obtained using SPSS 18 & data dimension is used to compute data into factors. Linear regression with its stepwise model is used to form the relation and influence of academic staffs job gratification on job performance of SALU Khairpur. Work and Achievement relation with job performance is effective among all variables. These two showed a strong & optimistic relation with job performance. This research study is taken in SALU Khairpur Mirs.
Accurate pathological patch classification with regression

Histopathological image analysis is important for cancer diagnosis. However, current computer cannot support the end-toend prediction of whole slide images (WSI) with gigapixel resolution. Most of current works slice high-resolution WSI into patches and classify them as tumor/normal tissues. However, the classification based on patches only uses category labels according to the proportion of cancer cells. For example, if a patch contains more than 50% cancer cells, its label is “tumor”, otherwise it is “normal”. Obviously, although this scheme can achieve good classification performance, it is unreasonable because doctors do not simply classify patches by whether the proportion of cancer cells is above 50% or not. If the ratio information of cancer cells in the patches can be fully utilized, the rationality and accuracy can be improved. In this article, we firstly notice cancer cell ratio information and propose a new model structure, combining classification and regression modules. We introduce a regression branch with reference to the fully connected layer structure of the classification model, and then combine the regression branch with the classification branch. When classifying the patches, the regression branch predicts the proportion of cancer cells, which can assist classification branch to achieve better patchlevel classification. In addition, we find that the baseline classification branch and the regression-assisted classification branch have different properties for tumor/normal patches. Combining the two branches for final prediction can further improve the classification performance. We apply our method to tumor/normal prediction on two large datasets and achieve better classification performance than state-of-the-art methods. Source code is available at https://github.com/ICLAB/ RACN.
Human-Machine Teaming: A Movement-Focused Approach

Abstract To cope with today’s frequent, extensive and unpredictable changes, humans and machines need to work together on the same team. Team organization and management called for now is to develop a truly adaptable network without any constraints. Movement works as a communication tool for the human-machine team, and in addition, movement will bring emotional harmonization between humans and machines and psychological satisfaction and happiness to humans. Although instinct has been neglected in traditional engineering, it plays an important role to coordinate many body parts and balance our bodies, and for interactive holistic perception and for making better decisions. Emerging reservoir computing will produce extremely small devices so they will work together on us and enable us to interact directly with the outside world through our bodies. And such human-machine team will motivate us to challenge rehabilitation and healthcare, which, therefore, will become a kind of a game, But to achieve this goal, a holistic and quantitative performance indicator is necessary. Euclidean Space approach requires orthonormality and units. But to manage movements, we must be free from these constraints. Therefore, Mahalanobis Distance-Pattern Approach, which is non-Euclidean, is proposed.
Quantum Computers Managed To Surpass Ordinary Computers

Interest in constructing quantum computer systems has received big momentum in latest years, and feverish work is underway in many components of the world. In 2019, Google's lookup group made a foremost leap forward when their quantum pc managed to resolve a undertaking a ways extra shortly than the world's great supercomputer.
Epsilon-near-zero plasmonic waveguides to achieve multi-qubit quantum entanglement

Multi-qubit entanglement is of paramount importance to perform truly secure quantum optical communication operations. In this work, we present efficient long-range entanglement mediated by plasmonic waveguides operating at their cut-off frequency, where they exhibit an effective epsilon-near-zero (ENZ) response. Only the excited ENZ mode at plasmonic waveguides has a strong and homogeneous electric field distribution compared to different modes excited at groove and rod waveguides, resulting in an efficient two- and multi-qubit entanglement that can persist over extended time periods and long distances. Efficient multipartite entanglement from three equidistantly arranged two-level quantum emitters (qubits) inside the ENZ waveguide is also demonstrated by using three different multi-qubit entanglement criterions, i.e., pairwise concurrence, negativity, and genuine multipartite entanglement. The findings of this work stress the importance of ENZ plasmonic waveguides as an ideal nanophotonic platform to support efficient quantum optical effects.
Visualizing Uncertainty in Weather Forecasts

Uncertainty bears an inherent relationship to weather forecasting. Even with advances in technology and mathematical modeling improving weather forecasts, communicating risks to the public is still challenging. This systematic review investigates design attributes that influence the expression of uncertainty visualization in weather forecasts. A total of 14 publications met the inclusion criteria. This review revealed six categories of design features that can inform how viewers interpret the uncertainty conveyed in weather forecast displays. These design features also provide us with an initial basis for developing guidelines. We discuss how the features might be used and the importance of doing so.
Musique Concrète Étude (Intermediate)

This chapter describes a lesson in which students create a one-minute piece of electronic music by editing, looping, reversing, changing pitch, changing speed, and spatializing a recording of a noninstrumental sound. This compositional activity was designed for first-year college music composition and audio production majors. First, this activity will help students learn to use a microphone to record their owns sounds and/or download open source material. Second, without the need to have experience with notation, melody, and harmony, this lesson allows students to engage with fundamental issues that composers face and can broaden their concept of what constitutes a piece of music.
Human epithelial-2 cell image classification using deep unsupervised learning and gradient boosting trees

In this paper, an ensemble learning framework is proposed for HEp-2 cell images, aiming to making use of both handcrafted features and deep learning-based methods. Firstly, deep unsupervised learning is employed to extract features. Then, a gradient boosting trees-based classifier is trained using both handcrafted features and deep learning-based features. Extensive experiments are conducted on benchmark datasets to test the efficiency and robustness of the proposed framework. Experiment results demonstrate hat the proposed framework yield excellent performances compared with existing deep learning-based models.
Perancangan Sistem Informasi Berbasis Web pada Home Industri Spectrum Production

In this modern era, business people take advantage of sophisticated technology in order to advance and develop to compete with others. One small industry, namely Spectrum Production, is still lacking in publishing it to the public. This research aims to design an information system based on the Spectrum Production website. The method used in this research is the waterfall method. The stages carried out in this study include needs analysis, system design stage, use case diagrams for admins, use case diagrams for users, activity diagrams, display design, implementation, and black box testing. The features displayed include a main page containing an overview of Spectrum Production, a profile page explaining the founding of Spectrum, a production photo page and an event photo explaining about service photos available at Spectrum, our contact page containing telephone numbers, emails, etc. The results show that the website can be applied and useful for Spectrum Production for socialization and media promotion as well as improvement in business. As well as providing the public with easy and very efficient access to information about Spectrum Production.
Developing a New Method for the Detection of the Cancerous Breast Mass

Breast cancer is now one of the main sources of death among women throughout the world. The detection of cancerous tumors in mammographic images is a great challenge for radiologists and physicians. It requires precise, experience and time. Fortunately, the evolution of science enabled the development of medical imaging techniques which are very efficacious for the benefit of health in order to detect any abnormalities in the breast parenchyma. It is in this context that our work fits. In fact, in this paperwe have implemented a computer-assisted diagnostic system (CAD) aiming at facilitating the early detection of this cancer. The developed method proceeds in three steps: pretreatment mammograms to enhance the segmentation step, segmentation based on morphological operators to detect breast masses. Finally, the segmentation of the breast contour based on the method of Marching Square was adopted. Our approach has been tested on the basis of pictures “MIAS” showing its efficiency. In addition, a graphical interface was performed to facilitate the task for radiologists.
Effect of Aerobic Exercise Intensity on Metabolic Risk Factors and Myokines in Obese People

Purpose: In this study is to examine changes in metabolic risk factors (TC, TG, LDL-C, HDL-C, BMI, fasting blood glucose, fasting insulin and HOMA-IR) and Myokines (Irisin and FGF-21) according to exercise intensity through long-term aerobic exercise for obese men.BR Method: Subjects of the study calculated individual VO2max by exercise load test, A: low-intensity aerobic exercise group, VO2R 40% (low-intensity exercise group; LIEG), B: medium-intensity aerobic exercise group, VO2 60% (middle-intensity exercise group; MIEG), C: high-intensity aerobic exercise group, VO2R 80% (high-intensity exercise group; HIEG), D: control group (CG). For aerobic exercise, the treadmill speed was adjusted to maintain about ±3 of the target heart rate until the time spent 300kcal at the corresponding exercise intensity with the individual target heart rate by wearing a wireless heart rate device. In consideration of exercise adaptability, exercise time and intensity were recalculated after 6 weeks and applied for 12 weeks, 3 times a week.BR Results: TG, LDL-C, HDL-C, BMI, Glucose, Insulin, and HOMA-IR showed significant decreases in LEDG and HIEG, excluding TC. In MIEG of exercise treatment, the duration of exercise treatment showed a greater reduction after 12 weeks than after 6 weeks. In addition, changes in Irisin and FGF-21 also showed the most effective increase in MIEG, and the improvement of myokine was higher after 12 weeks of aerobic exercise.BR Conclusion: Even with the same exercise in the exercise metabolism of obese people, 12 weeks of moderate-intensity aerobic exercise is considered the most effective exercise treatment for myokine expression and lipid relief by accelerating the change of metabolic risk factors and the browning of white fat.
Implementation of OSI Layer Based on Interactive Education Media

The design of basic network learning media using computer technology based on interactive educational media aims to facilitate the introduction of basic networks using interactive educational media so that they are more interested and able to follow and pay attention to the explanation conveyed through an OSI Layer work process in learning the basic network. In order to make it easier for readers, and faster to master and be able to practice for students who want to learn about the basic network introduction to the OSI Layer.
Research on Feature Description Based on Global Gray Order Pattern

In view of the problem that the LIOP feature description algorithm based on global gray-scale sequence mode makes insufficient use of local patch information and the feature dimension and the number of sampling points increase exponentially when constructing feature descriptors, an improved algorithm is proposed. Firstly, LIOP detection operator is used to extract the local features of the image, and the local features are preliminarily matched. Then, the operator is used to detect the feature points in the scale space, and the obtained feature points are grouped according to the local area features of the image, and the matching operation of feature point sets is carried out in the local area that has been successfully matched. It is insensitive to image rotation and spatial changes, and can adaptively aggregate coding features according to multi-resolution features of images. Color images are classified by texture and scene. Experiments show that this method improves the description ability of existing LIOP methods for color images.