Reviewerly/exHarmony huggingface dataset:
Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout

Abstract Using optical hardware for neuromorphic computing has become more and more popular recently, due to its efficient high-speed data processing capabilities and low power consumption. However, there are still some remaining obstacles to realizing the vision of a completely optical neuromorphic computer. One of them is that, depending on the technology used, optical weighting elements may not share the same resolution as in the electrical domain. Moreover, noise of the weighting elements are important considerations as well. In this article, we investigate a new method for improving the performance of optical weighting components, even in the presence of noise and in the case of very low resolution. Our method utilizes an iterative training procedure and is able to select weight connections that are more robust to quantization and noise. As a result, even with only 8 to 32 levels of resolution, in noisy weighting environments, the method can outperform both nearest rounding low-resolution weighting and random rounding weighting by up to several orders of magnitude in terms of bit error rate and can deliver performance very close to full-resolution weighting elements.
Joint Reasoning for Multi-Faceted Commonsense Knowledge

Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at this https URL.
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks

The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks. In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). SPEQ is a knowledge distillation training scheme; however, the teacher is formed by sharing the model parameters of the student network. We obtain the soft labels of the teacher by randomly changing the bit precision of the activation stochastically at each layer of the forward-pass computation. The student model is trained with these soft labels to reduce the activation quantization noise. The cosine similarity loss is employed, instead of the KL-divergence, for KD training. As the teacher model changes continuously by random bit-precision assignment, it exploits the effect of stochastic ensemble KD. SPEQ outperforms the existing quantization training methods in various tasks, such as image classification, question-answering, and transfer learning without the need for cumbersome teacher networks.
When Do Curricula Work

Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standard i.i.d. training. In this work, we set out to investigate the relative benefits of ordered learning. We first investigate the implicit curricula resulting from architectural and optimization bias and find that samples are learned in a highly consistent order. Next, to quantify the benefit of explicit curricula, we conduct extensive experiments over thousands of orderings spanning three kinds of learning: curriculum, anti-curriculum, and random-curriculum -- in which the size of the training dataset is dynamically increased over time, but the examples are randomly ordered. We find that for standard benchmark datasets, curricula have only marginal benefits, and that randomly ordered samples perform as well or better than curricula and anti-curricula, suggesting that any benefit is entirely due to the dynamic training set size. Inspired by common use cases of curriculum learning in practice, we investigate the role of limited training time budget and noisy data in the success of curriculum learning. Our experiments demonstrate that curriculum, but not anti-curriculum or random ordering can indeed improve the performance either with limited training time budget or in the existence of noisy data.
CARACTERIZAÇÃO E DEMANDA DE UM SERVIÇO DE ATENDIMENTO PSICOLÓGICO ON-LINE NO CONTEXTO DA PANDEMIA DE COVID-19

The COVID-19 pandemic has been considered the most serious and important public health crisis at this time. In addition to the alarming rates of morbidity and mortality worldwide, this disease has been triggering various types of psychological distress, due to social isolation, fear of falling ill or contaminating friends or family. Therefore, it is necessary to develop psychological treatment services for those who have some form of mental disorder resulting from this pandemic and its effects. This paper describes the experience and results of an online psychological care service developed by the Municipal Department of Health in Guaratinguetá, in the state of Sao Paulot, hat aimed to provide a psychological treatment to the general population and health professionals in order to alleviate suffering and favor a better quality of life. The results show that 145 people were treated remotely, predominantly female (79.3%), aged between 41-59 years (35.2%) and between 60-80 years (22.1%). A large part of the population treated has completed high school (38.0%), is a housewife (26.9%) or is unemployed (16.6%). About the complaint, it was observed that the vast majority referred to anxiety (45.4%) or depression (18.9%). From these data, it is concluded that the continuity and expansion of this type of service is necessary for protection and promotion of the mental health of the population in general and, more specifically, of the health professionals who are on the front line of combat to the COVID-19 pandemic.
Pixel-level tumor margin assessment of surgical specimen in hyperspectral imaging and deep learning classification

Surgery is a major treatment method for squamous cell carcinoma (SCC). During surgery, insufficient tumor margin may lead to local recurrence of cancer. Hyperspectral imaging (HSI) is a promising optical imaging technique for in vivo cancer detection and tumor margin assessment. In this study, a fully convolutional network (FCN) was implemented for tumor classification and margin assessment on hyperspectral images of SCC. The FCN was trained and validated with hyperspectral images of 25 ex vivo SCC surgical specimens from 20 different patients. The network was evaluated per patient and achieved pixel-level tissue classification with an average area under the curve (AUC) of 0.88, as well as 0.83 accuracy, 0.84 sensitivity, and 0.70 specificity across all the 20 patients. The 95% Hausdorff distance of assessed tumor margin in 17 patients was less than 2 mm, and the classification time of each tissue specimen took less than 10 seconds. The proposed methods can potentially facilitate intraoperative tumor margin assessment and improve surgical outcomes.
Human-Aware Robot Navigation by Long-Term Movement Prediction

Foresighted, human-aware navigation is a prerequisite for service robots acting in indoor environments. In this paper, we present a novel human-aware navigation approach that relies on long-term prediction of human movements. In particular, we consider the problem of finding a path from the robot's current position to the initially unknown navigation goal of a moving user to provide timely assistance there. The navigation strategy has to minimize the robot's arrival time and at the same time comply with the user's comfort during the movement. Our solution predicts the user's navigation goal based on the robot's observations and prior knowledge about typical human transitions between objects. Based on the motion prediction, we then compute a time-dependent cost map that encodes the belief about the user's positions at future time steps. Using this map, we solve the time-dependent shortest path problem to find an efficient path for the robot, which still abides by the rules of human comfort. To identify robot navigation actions that are perceived as uncomfortable by humans, we performed user surveys and defined the corresponding constraints. We thoroughly evaluated our navigation system in simulation as well as in real-world experiments. As the results show, our system outperforms existing approaches in terms of human comfort, while still minimizing arrival times of the robot.
The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river

Abstract Sediment transport is one of the most important issues in river engineering. In this study, the capability of the Kernel Extreme Learning Machine (KELM) approach for predicting the river daily Suspended Sediment Concentration (SSC) and Discharge (SSD) was assessed. Three successive hydrometric stations of Mississippi river were considered and based on the sediment and flow characteristics during the period of 2005–2008. Several models were developed and tested for SSC and SSD modeling. For improving the applied model efficiency, two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of modeling based on stations' own data (state 1) and previous stations' data (state 2) were considered. The single and integrated KELM model results comparison indicated that the integrated WT and EEMD-KELM models resulted in more accurate outcomes. Results showed that data processing with WT was more effective than EEMD in increasing the models' efficiency. Data processing enhanced the models' capability by up to 15%. The results showed that the state 1 modeling led to better results, however, using the integrated KELM approaches the previous stations data could be applied successfully for SSC and SSD modeling when the stations' own data were not available. HIGHLIGHT The suspended sediment concentration (SSC) and suspended sediment discharge (SSD) were predicted via artificial intelligence approach in successive hydrometric stations. The data pre-processing impacts on models' efficiency improvement was assessed. The sensitivity analysis showed the most effective subseries was obtained from pre-processing models.
Evolution of the Kandilli Observatory and Earthquake Research Institute (KOERI) Seismic Network and the Data Center Facilities as a Primary Node of EIDA

Abstract As the earliest institute in Turkey dedicated to locating, recording, and archiving earthquakes in the region, the Kandilli Observatory and Earthquake Research Institute (KOERI) has a long history in seismic observation, which dates back to the installation of its first seismometers soon after the devastating Istanbul earthquake of 10 July 1894. For over a century, since the deployment of its first seismometer, the KOERI seismic network has grown steadily in time. In this article, we present the KOERI seismic network facilities as a data center for the seismological community, providing data and services through the European Integrated Data Archive (EIDA) and the Rapid Raw Strong-Motion (RRSM) database, both integrated in the Observatories and Research Facilities for European Seismology (ORFEUS). The objective of this article is to provide an overview of the KOERI seismic services within ORFEUS and to introduce some of the procedures that allow to check the health of the seismic network and the quality of the data recorded at KOERI seismic stations, which are shared through EIDA and RRSM.
A GLCM based Feature Extraction in Mammogram Images using Machine Learning Algorithms

Introduction: Most Indian women are suffering from Breast Cancer.The simple and efficient screening used for Breast Cancer (BC) is Mammograms.Mammogram images are used to detect BC in the early stages.Objective: The main objective of our research is to detect the BC in early stages using Gray Level Co-occurrence Matrix (GLCM) with Machine Learning Algorithms.Methods: Our proposed system is a two-step process which includes feature extraction and classification.Features are extracted from the Mammographic Image Analysis Society (MIAS) database by using a texture-based descriptor called GLCM.These features are passed to classifiers called K-Nearest Neighbor (KNN), Random Forest (RF) and Gradient Boosting by considering 30% as testing data size. Results:The experiments are done as follows: GLCM+RF, GLCM+KNN and GLCM+ Gradient Boosting and the performance of these classifiers are calculated by finding accuracy metric. Conclusion:The conclusion is that GLCM features with KNN classifier give better results than other classifiers.
Effectiveness of traditional and online learning: comparative analysis from the student perspective

The article discusses challenges of effective learning in classroom and online environments from the student perspective. The aim of the study is to analyse university students’ perception of advantages and disadvantages of digital education. The article presents comparative analysis of traditional classroom and online academic environments as viewed by first- and second-year Russian students majoring in management and economics. The study is a questionnaire-based research. Quantitative and qualitative research methods were applied to achieve study’s objectives. Special attention was paid to the analysis of students’ perception of a modern university teacher role in the classroom and online settings. The findings prove positive attitude of the respondents to digital learning and showed students’ awareness of the barriers to effective distance learning, including their laziness and inability to concentrate. The research results can help practitioners and educators better understand students’ learning needs and assist them in achieving academic success.
Utilization of Landsat-8 Imagery and Aeromagnetic Data for Deciphering Alteration Zones and Structures: Implications for Mineral Exploration in the Southeastern Desert of Egypt

Remote-sensing (RS) and aeromagnetic data implemented notable information for mineralization potentiality detection of ore deposits within Al-Allaqi region of the southeastern Desert of Egypt. Several approaches of remote-sensing and aeromagnetic data were implemented. Results of band ratios (BR) and Principal Component Analysis (PCA) practicing Landsat-8 successfully imaging the hydrothermally altered zones and the structures outlined by lithologic contacts, faults, and porphyry features (dyke-like structures). Lineaments well extracted using aeromagnetic datasets. CET system method was applied for generating a heat map of the structural complexity. Likewise, the CET Porphyry Analysis system was performed to extract the likely near-circular porphyry features that revealing a probable site of mineralization and to validate the remote sensing observations. Integrated outcomes showed that areas of highly complex structures, density (fractures/faults) are consistent with the detected hydrothermally altered areas that are rivaled with mining zones in Al-Allaqi region of the Southeastern Desert of Egypt. This approach can be broadly appropriate to other divisions of the Southeastern Desert of Egypt for mineral prospecting.
Relationship between first-order coherence and the maximum violation of the three-setting linear steering inequality for a two-qubit system

Coherence and nonlocal correlations are two fundamental features of quantum mechanics and the relations between them have received much attention recently. While the relations of coherence to Bell nonlocality and to entanglement have been established, an interesting question left open is how to quantitatively characterize the relation of coherence to steering, which is one kind of nonlocal correlation lying between Bell nonlocality and entanglement. In this paper, we address this question by considering the first-order coherence and the three-setting linear steering inequality for a two-qubit system. Interestingly, we find that the first-order coherence for both subsystems has a complementary relation with the maximum violation of the three-setting linear steering inequality. A weighted sum of their squares keeps invariant under unitary operations, although each of them changes in general. This suggests that the maximum violation of the three-setting linear steering inequality can be taken as a measure of hidden coherence unveiled by unitary operations.
IMPACT OF COVID19 ON ACCOUNTING PROFESSION FROM THE PERSPECTIVE OF A SAMPLE OF HEAD OF ACCOUNTING DEPARTMENTS WITHIN KUWAITI MANUFACTURING SECTOR

Currents study aimed at examining the impact of COVID19 on accounting profession from the perspective of accounting managers in Kuwaiti manufacturing sectors. Quantitative approach was adopted in order to gather needed data – even though researcher preferred qualitative approach but health precautions prevented that – a questionnaire was adopted and uploaded through Google forms. After application process, (177) individuals responded to the questionnaire and study indicated that COVID19 and its spread managed to increase the difficulty on accounting profession due to the decline and organizational activities and the inability to reach clear and precise data, in addition to that, study proved that what increased the burden on accounting profession that organizations didn't have the full ability to perform disclosure as according to standards and rules due to the lockdown and the continuous limiting of commercial and financial activities of organizations. Study recommended that the accounting sector must take advantage of the opportunity to learn from this experience, which needs innovation in solutions and management, to benefit from the lessons of crises.
Effect of health outcome on economic growth in sub-Saharan Africa: a system generalized method of moment approach

Purpose Some progress have been made over time in improving health conditions in Sub-Saharan Africa (SSA). There are, however, contradicting reports on the relationship between health outcomes and economic growth in the region. The paper aimed at assessing the effect of health outcome on economic growth in SSA. Design/methodology/approach Data for 41 countries from 2000 to 2018 were obtained from WDI and WGI and analyzed using system generalized method of moment (sGMM) which is appropriate for the present scenario. AR(1) and AR(2) tests were used to assess the validity of the model while Sargan and Hansen tests were adopted to examine the validity of the instrumental variables. The robustness of the estimation was confirmed using the pooled OLS and fixed effect regression. Findings Health outcome (proxied by life expectancy), lagged GDP per capita, capital formation, labor force (LF), health expenditure (HE), foreign direct investment (FDI) and trade openness (TOP) significantly affected economic growth emphasizing the importance of health in the process of economic growth in the region. AR(1) and AR(2) tests for serial correlation and Sargan/Hansen tests confirmed the validity of the estimated model and the instrumental variables respectively. Robustness of the GMM results was established from the pooled OLS and the fixed effect model results. Social implications Improvement in the national health system possibly through the widespread adoption of National Health Insurance, increase government spending on healthcare alongside increased beneficial trade and ease of doing business to facilitate investment were recommended to enhance. Originality/value The study used up-to-date data with appropriate methodology.
Computer Assisted Instruction System under Artificial Intelligence Technology

In order to promote the development of intelligent teaching system, artificial intelligence technology is combined with computer assisted instruction (CAI) system to produce intelligent CAI (ICAI), and the design of ICAI is studied in order to facilitate the application and implementation of intelligent teaching system. First, there is an introduction of the basic concepts, related theories, and implementation principles of ICAI. Then, the requirements of ICAI are analyzed in detail on the basis of studying the relevant technologies required by ICAI, including the functional and non-functional requirements. The subsystem is set based on the different needs of the target users (administrator, teacher, and student users). Finally, the general and functional structure of ICAI are designed, and there are tests for the system functions. The results show that the system takes Java Server Pages (JSP) as the development language, Struts2 as the technical framework, and SQL Server 2008 as the back-end database, which realizes a teaching platform mainly for teachers, students, and administrators. After testing, the overall operation of the system is good, and the functional test results of the login module, homework release, and learning resource upload meet the software design requirements. The response time of the system prolongs, the number of users who can’t successfully log in grows, and the utilization of server central procession unit (CPU) and memory utilization both increase, which are caused by the increasing number of concurrent users. The results indicate that the combination of CAI and artificial intelligence (AI) technology can meet students’ online teaching demands, break the time-space limitation of traditional teaching, and provide important reference value for the reform of teaching mode and the improvement of teaching quality