Reviewerly/exHarmony huggingface dataset:
Multi-Depth Learning with Multi-Attention for fine-grained image classification

Compared with the traditional image classification task, fine-grained image classification has the difficulty of small differences between classes and large differences within classes. In view of this difficulty, attention proposal has been widely used in fine-grained image classification. However, traditional attention proposal has to localize first and then processing. Model needs to run step by step and the attention focusing method is single. This paper proposed a model (MAMDL, Multi-Attention-Multi-Depth-Learning) which combines multiple attention mechanisms and multi network parallel learning. The advantage of MAMDL is that it can first learn end-to-end. Secondly, the multiple attention mechanisms can effectively combine four attention mechanisms to improve the network's ability to process local features. Finally, this paper focuses on the attention found in the backbone network, Feature extraction from branch convolution neural networks with different depths enhances the classification performance of the model. The experimental results show that MAMDL outperforms mainstream fine-grained image classification methods on the fine-grained image classification dataset CUB-200, Stanford dogs and Stanford cars.
Rudiments of Modal Logic

This last chapter aims to provide a concise presentation of modal logic, the logic of necessity and possibility. A modal language is a language that contains, in addition to the symbols of a propositional or predicate language, the modal operators Open image in new window and ◊, which mean respectively ‘it is necessary that’ and ‘it is possible that’.
Prospects for digital evaluation of the dissertation

Currently, one of the most important components of scientific activity is the writing and subsequent defense of a candidate's / doctoral dissertation. It is revealed that, despite the rapid digitalization of the educational process in the scientific paradigm of the whole world, including Russia, there is no single digital assessment of the dissertation. The current system of evaluation of a dissertation based on the one-dimensional designation "yes" or "no" does not allow to objectively and impartially determine the scientific value of the dissertation. The author proposed a methodology for evaluating dissertations on a one-hundred-point scale, where the researcher with the highest score on the defense of the dissertation (91-100) would be provided with financial support from the state to continue promising scientific research, while at the same time being an advantage in the competition for his election to the Russian Academy of Sciences. The author is critical of the conditions of the state and prospects of the development of science in Russia, considering it unfair to neglect the system of evaluation in the defense of dissertations, which leads to a constant outflow of young researchers abroad. In the perspective of applying the digital assessment of the dissertation, the support of talented scientists in obtaining high scores in the defense of the dissertation at the local, regional and federal levels is considered.
Study on The Relationship Between Meteorological Factors and Photovoltaic Power Generation Efficiency and Influence Mechanism

Abstract The paper uses the minute-by-minute photovoltaic power data of a photovoltaic power station in Lanzhou of Huadian from June 1, 2018 to May 31, 2019 and the meteorological observation data of the Lanzhou National Climate Observatory in the same period to analyze the impact of meteorological elements on the power of photovoltaic power plants. The results show that the daily and hourly output power of photovoltaic power plants are highly positively correlated with sunshine time and light intensity. The longer the sunshine time and the greater the light intensity, the more the output power of the module; the cloud cover and relative humidity weaken the photovoltaic power, and The influence of low cloud cover is greater than the influence of total cloud cover, and cloud shape also has a significant influence; the influence of temperature on the power of photovoltaic power plants is more complicated, and the increase of temperature reduces the power of photovoltaic power, and the mechanism needs to be further studied.
A treatment engine by multimodal EMR data

In recent years, with the development of electronic medical record (EMR) systems, it has become possible to mine patient clinical data to improve medical care quality. After the treatment engine learns knowledge from the EMR data, it can automatically recommend the next stage of prescriptions and provide treatment guidelines for doctors and patients. However, this task is always challenged by the multi-modality of EMR data. To more effectively predict the next stage of treatment prescription by using multimodal information and the connection between the modalities, we propose a cross-modal shared-specific feature complementary generation and attention fusion algorithm. In the feature extraction stage, specific information and shared information are obtained through a shared-specific feature extraction network. To obtain the correlation between the modalities, we propose a sorting network. We use the attention fusion network in the multimodal feature fusion stage to give different multimodal features at different stages with different weights to obtain a more prepared patient representation. Considering the redundant information of specific modal information and shared modal information, we introduce a complementary feature learning strategy, including modality adaptation for shared features, project adversarial learning for specific features, and reconstruction enhancement. The experimental results on the real EMR data set MIMIC-III prove its superiority and each part's effectiveness.
Exploration and Exploitation in Organizational Cybersecurity

This study examines the cybersecurity responses by organizations to threats. Based on the notions of exploration and exploitation, this study developed a 2 × 2 matrix to characterize organizational actions as Surviving, Investigating, Reinforcing, and Balancing. Using textual data gathered from the annual 10–K reports of 87 organizations, this study mapped the explorative and exploitative behaviors of organizations and characterized the cybersecurity responses with the 2 × 2 matrix. This study also identified the changes in cybersecurity responses over multiple time periods, and the patterns of shifts between the quadrants in the 2 × 2 matrix as organizations adapted their cybersecurity responses over time. Several implications for research and practice are discussed.
Assessment of users satisfaction of information provision among agricultural researchers in Nigeria

In order to improve the performance of agriculture in Nigeria different agricultural research institutions were established.Realising the fact that economic and social development is based on progress in agricultural as well, these agricultural institutions have been striving to ensure food security in the country.But all these efforts have not yielded the desired result as the agricultural output in Nigeria can still not compete favourably with developed countries.Though, it has been established that there can be no research without information.Information is one of the components to achieve sustainable progress in agricultural research.Could it be that the agricultural researchers are not getting enough information for their research activities or they are not satisfied with information provided for research?Thus this study assessed agricultural researcher's satisfaction in terms of information facilities, sources, resources and contents using the researcher in all agricultural research institutions affiliated to agricultural research council of Nigeria (ARCN).The findings revealed that satisfaction with information facilities and information resources are high; but there is need for upgrade in information content and services for better satisfaction.Thus the study made some recommendations for better satisfaction so that there can be increased in research output which will in turn improve agricultural output in Nigeria.
Medical Image Enhancement Technique Using Multiresolution Gabor Wavelet Transform

Medical images are applied for analysis and diagnosis of particular medical disorder or diseases. Hence, the medical image enhancement technique is necessary and challenging for further processing through computer vision systems. It assists in further processing of medical images for segmentation, detection and prediction of certain diseases such as cancer, tumor and any other disorder. Most of the medical images obtained through various sources are dark and seem to be noisy that requires efficient image enhancement technique that preserves the content of the images. In this paper, the image enhancement technique through multiresolution Gabor wavelet transform is presented. Gabor wavelet transform has demonstrated multiresolution capabilities with better texture enhancement that helps in quality improvement in medical images. Experiments based on public dataset reveal better performance with respect to qualitative and quantitative analysis. Experimental results on several low illuminated medical images demonstrate best results in terms of enhancement parameters and visual testing. Finally, the obtained outcomes are compared with the prominent methods published in the literature.
Recognition of Indonesian Sign Language Alphabets Using Fourier Descriptor Method

Feature extraction is a process to search for special features of an object that will distinguish the characteristics between one object and another. In this study, the Fourier Descriptor method was used for extracting the feature of the Indonesian Sign Language (BISINDO) images. This research has been performed by implementing four main steps, i.e., pre-processing by converting RGB images to grayscale and binary ones, and closing operation for smoothing the images; contour detection using Moore's algorithm; feature extraction implementing Fourier descriptor with 5 kinds of coefficient i.e., 2, 5, 10, 25, and 50 to represent the feature of the images; and lastly feature recognition process by computing the image similarity using Euclidean Distance. 1820 images divided into 4 kinds i.e., standard, scaled, rotated, and translated images have been tested with 130 images of training data. Based on the test result of 130 standard images for each coefficient, the best accuracy is obtained at coefficient 25 and 50 with a similar accuracy of 96.92%. In addition, the recognition performance of Fourier descriptor and Euclidean distance reached up to above 72% in average for standard and scaled images.
Slimming ResNet by Slimming Shortcut

Conventional network pruning methods on convolutional neural networks (CNNs) reduce the number of input or output channels of convolution layers. With these approaches, the channels in the plain network can be pruned without any restrictions. However, in the case of the ResNet based networks which have shortcuts (skip connections), the channel slimming of existing pruning methods is limited to the inside of each residual block. Since the number of Flops and parameters are also highly related to the number of channels in the shortcuts, more investigation on pruning channels in shortcuts is required. In this paper, we propose a novel pruning method, Slimming Shortcut Pruning (SSPruning), for pruning channels in shortcuts on ResNet based networks. First, we separate the long shortcut into individual regions that can be pruned independently without considering its long connections. Then, by applying our Importance Learning Gate (ILG) which learns the importance of channels globally regardless of channel type and location (i.e., in the shortcut or inside of the block), we can finally achieve an optimally pruned model. Through various experiments, we have confirmed that our method yields outstanding results when we prune the shortcuts and inside of the block together.
Communication in Narrative Frames Designing with Smart Material

Design oriented by new materials and the experience they provide acts effectively in different frames of narrative design and in such design process, materials represent design as dynamic intermedia. With development of smart technology, smart materials become a new kind of orientation in such context and support design process in different narrative frames in communication and sharing ideas among design participants. A significant feature in this process is, that the communication is divided in to two layers, including the dialogue between the participants and between smart material and participants, for the smart materials give feedbacks and reactions. Based on this situation, this paper constructs a communication framework based on the trend of smart material and relative technologies. This research is based on literature review and empirical design studies with biomaterial, for its properties of reactions as one of the kinds of smart material in generalized concept.
Parametric Spectral Filters for Fast Converging, Scalable Convolutional Neural Networks

Using spectral multiplication to compute convolution in neural networks has been investigated by a number of researchers because of its potential in speeding up computations for large images. However, previous methods require the learning of arbitrarily large convolution filters in the spectral domain, causing two untenable problems: an explosion in the number of trainable parameters per filter and an inability to reuse filters across images of differing sizes. To address this, we propose the usage of spectral parametric functions to represent massive spectral domain filters with only a few trainable parameters. Our empirical analysis suggests that the proposed functions maintain the benefits of arbitrarily large filters (such as improved rate of convergence in training, accuracy, and stability) while relying on significantly fewer trainable parameters.
Cell proliferation detection based on deep learning

Cell proliferation level is important in clinical diagnosis. Analysis of cell proliferation level can help to judge the development trend of patient's condition, which is helpful for diagnosis. In order to evaluate the level of cell proliferation, it is necessary to calculate the number and proportion of proliferating nuclei. However, when the number of nuclei is large, it will bring a great pressure on doctors, and the accuracy rate will also decline. To solve these problems, this paper proposes a scheme for automatic detection and counting of cell nuclei, which is large in number and densely distributed. Our dataset is obtained from mouse liver cells, a total of 136 samples, each containing 100-300 nuclei and the proliferation of nuclei are stained by immunohistochemical staining, of which 120 are trained using convolution neural network and 16 samples are tested to evaluate the effects of the models. Three models were trained by RetinaNet with backbone networks of Vgg6, ResNet50 and ResNet101 respectively, and compared with Image Pro Plus 6.0. Experiments show that our models achieve higher accuracy compared with 67.2% obtained by manual using Image-Pro Plus 6.0 for detection and counting. It can be seen that in terms of detection accuracy, our models are better than the analysis software widely used in hospitals, effectively solving the problems of long manual detection time and low accuracy. It can effectively help doctors to evaluate the level of cell proliferation, and then quickly make a corresponding diagnosis.
Research on the Application of Information Technology in Educational Decision

Information is the prerequisite for decision-making, and information technology is the guarantee for the effectiveness of scientific decision-making. Education is an important force to promote social progress, and the key to the development of education is to establish correct training goals. In the research of technical analysis of current education status, it is found that the dispersion of expected goals affects the realization of education goals. Only by considering the comprehensive effects of multiple factors and making reasonable decisions can we effectively avoid the deviation of training goals and enhance the effect of education.
‘Dismay’ at global standards for school geoscience

Despite growing demand for geo-scientists around the world, the attainment achieved in school-level earth-science education is bleak.
SQWA: Stochastic Quantized Weight Averaging For Improving The Generalization Capability Of Low-Precision Deep Neural Networks

Low-precision deep neural networks (DNNs) are very needed for efficient implementations, but severe quantization of weights often sacrifices the generalization capability and lowers the test accuracy. We present a new quantized neural network optimization approach, stochastic quantized weight averaging (SQWA), to design low-precision DNNs with good generalization capability using model averaging. The proposed approach includes (1) floating-point model training, (2) direct quantization of weights, (3) capturing multiple low precision models during retraining with cyclical learning rates, (4) averaging the captured models, and (5) re-quantizing the averaged model and fine-tuning it with low-learning rates. With SQWA training, we could develop the best performing QDNNs for image classification on ImageNet datasets and also for semantic segmentation on Pascal VOC 2012 dataset.
Measuring the Effects of Virtual Environment Design on Decision-Making

Recent research indicates an impairment in decision-making in immersive Virtual Reality (VR) when completing the Iowa Gambling Task (IGT). There is a high potential for emotions to explain the IGT decision-making behavior. The design of a virtual environment (VE) can influence a user's mood and hence potentially the decision-making. In a novel user study, we measure decision-making using three virtual versions of the IGT. The versions differ with regard to the degree of immersion and design of the VE. Our results revealed no significant impact of the VE on the IGT and hence on decision-making.
From Virtual to Real World: Applying Animation to Design the Activity Recognition System

Following the conventional pipeline, the training dataset of a human activity recognition system relies on the detection of the significant signal variation regions. Such position-specific classifiers provide less flexibility for users to alter the sensor positions. In this paper, we proposed to employ the simulated sensor to generate the corresponding signal from human motion animation as the dataset. Visualizing the corresponding items from the real world, the user can determine the sensor’s placement arbitrarily and obtain accuracy feedback as well as the classifier interface to get relief from the cost of a conventional training model. With the cases validation, the classifier trained by simulated sensor data can effectively recognize the real-world activity.
Fertile Soil for Intrapreneurship: Impartial Institutions and Human Capital

Intrapreneurs, entrepreneurial employees, constitute an important force behind innovations in the economy. Yet, what factors that promote intrapreneurship at the country level are an underdeveloped research area. This paper provides a seminal contribution regarding the methodological approach and the broad set of potential explanatory factors studied. Based on machine-learning techniques (LASSO and EBA methods), we investigate the influence of over 60 factors capturing institutional, demographic, cultural, and developmental factors. We find that the quality of government measured as impartiality, i.e., that the political institutions treat the citizens in a non-discriminatory fashion and do not favor some groups or individuals, and the level of human capital, measured as the average years of schooling, are the most important factors predicting the level of intrapreneurship across countries. Instrumental variable results support a causal interpretation. The findings emphasize the importance of policy to establish well-functioning and impartial institutions as well as to promote higher education.
Rule-Based Approach for Emotion Detection for Kannada Text

In today’s world, people have conversation or exchange of information through their local language easily. Since most of the consumers express their views on the product in their local languages, it has become important to build an efficient sentiment analysis model for them. Also, in the field of literature, sometimes we find difficulty in understanding the feelings expressed by the author in their biography or emotions involved in the stories written by them. To solve these kinds of problems, we propose a rule-based methodology to categorize the emotions involved in the documents written in Kannada language. The proposed model gives 85% accuracy which is found to be efficient when compared to the bag-of-words approach which gives 67% accuracy.
GAD: A Global Attraction Dataset and Attraction Classification Based on Residual Dense Convolutional Neural Networks

Over the past years, the development of big data have received great attention, and data growth continues to increase. Deep learning technologies have shown good performance in many practical applications. In this work, we proposed a new deep learning model with low complexity to deal with global attraction classification problem. Due to limited datasets in outdoor scenes, firstly, we constructed a new large-scale global attraction dataset (GAD), containing more than 380k images for the world's 250 famous attractions. Then we propose a new attraction classification method, where a residual dense module is integrated into a low-complexity convolutional neural network model. Our proposed method is demonstrated to show better performance than several state-of-the-art deep convolutional neural network models on our constructed GAD dataset. Index Terms-Artificial intelligence, classification, convolutional neural network, deep learning, residual dense block.
Machine Translation Based on Domain Adaptive Language Model

This study presents a domain adaptive language model based on adjustable parameters and domain interpolation. Meanwhile, a method of automatically determining test data domain by language model is proposed. Results show that the perplexity of the proposed language model is significantly lower than that of the baseline of KN smoothing language model on the cross-domain test set. In Chinese-English translation, the BLEU value of machine translation evaluation is also significantly higher than that of baseline model.
Performance-Based Adaptive Learning Rate Scheduler Algorithm

Neural networks have proved to be useful in a variety of applications in almost any major field of study. Training a neural network requires a repetitive process called hyperparameter tuning. The most important hyperparameter that should be tuned is the learning rate of the neural network. Adaptive learning rate algorithms have improved the efficiency of neural networks by improving the accuracy and minimizing the loss within less number of epochs. Here, a new algorithm called “performance-based adaptive learning rate” algorithm is shown and its various performance results on a dataset are performed to assess the efficiency of the algorithm.
Digital Paywall Design: Implications for Content Demand and Subscriptions

Most online content publishers have moved to subscription-based business models regulated by digital paywalls. But the managerial implications of such freemium content offerings are not well unders...