Reviewerly/exHarmony huggingface dataset:
Extreme Image Classification Algorithm Based on Multicore Dense Connection Network

Extreme images refer to low-quality images taken under extreme environmental conditions such as haze, heavy rain, strong light, and shaking, which will lead to the failure of the visual system to effectively recognize the target. Most of the existing extreme image restoration algorithms only handle the restoration work of a certain type of image; how to effectively recognize all kinds of extreme images is still a challenge. Therefore, this paper proposes a classification and restoration algorithm for extreme images. Due to the large differences in the features on extreme images, it is difficult for the existing models such as DenseNet to effectively extract depth features. In order to solve the classification problem in the algorithm, we propose a Multicore Dense Connection Network (MDCNet). MDCNet is composed of dense part, attention part, and classification part. Dense Part uses two dense blocks with different convolution kernel sizes to extract features of different sizes; attention part uses channel attention mechanism and spatial attention mechanism to amplify the effective information in the feature map; classification part is mainly composed of two convolutional layers and two fully connected layers to extract and classify feature images. Experiments have shown that the recall of MDCNet can reach 92.75% on extreme image dataset. At the same time, the mAP value of target detection can be improved by about 16% after the image is processed by the classification and recovery algorithm.
Group chat analysis of hoax detection during the covid-19 pandemic using the k nearest neighbors algorithm and massive text processing

Abstract Group chat is the most widely used choice of various short information. Besides being easy to send messages, sharing short messages in group chat is considered effective compared to sending massively to several users. The ease of sending short messages in group chat is often used as the spread of fake news and untrue news or hoaxes, especially during the Covid-19 pandemic, the information shared can be easily shared by anyone without seeing a valid source. The dissemination of information related to Covid-19 without a clear source is a dangerous act, because it can lead users into false information and endanger themselves. Fake message detectors have not been widely implemented in instant message applications, for this reason, there is a need for a detector and a machine to analyze activities in group chat and see whether the message is included in content containing fake news or not. If a group chat has a lot of fake news, you can be sure that the group chat is not good to follow. The use of the K-Nearest Neighbors algorithm is considered quite effective in classifying an object, the results can be determined whether it is included in fake news, miss-information news, or true news. The process of processing messages is carried out by the massive text processing method because the characteristics of the text are different for each user so that text processing can be maximized for later classification. As a result, group chat can be analyzed based on active time, user messages, user activity, and messages sent between users.
Does CSR add Value to Service Innovation Performance via HRM? A Conceptual Framework and Future Research Agenda

Drawing upon social exchange theory, the present study conceptualizes CSR as a strategic asset, and organizational CSR initiatives influence employee’s attitudes and behaviors, including citizenship and innovative behavior. CSR can be aligned with HR strategies to get employees boost and committed to their organization. CSR strategy from an employee perspective may become a rare, valuable, non-substitutable and inimitable resource that gives organizations’ strategic and sustainable competitive advantages. The study concludes CSR is a promising area that affects employee’s attitudes and behaviors within the framework of HRM. Current literature is inconsistent and lacks maturity. The present study integrates CSR with attitudes and behaviors from an innovation perspective. CSR as a strategic resource is an attraction for stakeholders, including employees.
Using neural networks to extend cropped medical images for deformable registration among images with differing scan extents

Abstract Purpose Missing or discrepant imaging volume is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT's and then test its use in DIR. Methods Using a training dataset of 409 head and neck CT's, we trained a generative adversarial network to take in a cropped 3D image and output an image with synthesized anatomy in the cropped region. The network used a 3D U‐Net generator along with Visual Geometry Group (VGG) deep feature losses. To test our technique, for each of the 53 test volumes, we used Elastix to deformably register combinations of a randomly cropped, full, and synthetically full volume to a single cropped, full, and synthetically full target volume. We additionally tested our method's robustness to crop extent by progressively increasing the amount of cropping, synthesizing the missing anatomy using our network, and then performing the same registration combinations. Registration performance was measured using 95% Hausdorff distance across 16 contours. Results We successfully trained a network to synthesize missing anatomy in superiorly and inferiorly cropped images. The network can estimate large regions in an incomplete image, far from the cropping boundary. Registration using our estimated full images was not significantly different from registration using the original full images. The average contour matching error for full image registration was 9.9 mm, whereas our method was 11.6, 12.1, and 13.6 mm for synthesized‐to‐full, full‐to‐synthesized, and synthesized‐to‐synthesized registrations, respectively. In comparison, registration using the cropped images had errors of 31.7 mm and higher. Plotting the registered image contour error as a function of initial preregistered error shows that our method is robust to registration difficulty. Synthesized‐to‐full registration was statistically independent of cropping extent up to 18.7 cm superiorly cropped. Synthesized‐to‐synthesized registration was nearly independent, with a −0.04 mm of change in average contour error for every additional millimeter of cropping. Conclusions Different or inadequate in scan extent is a major cause of DIR inaccuracies. We address this challenge by training a neural network to complete cropped 3D images. We show that with image completion, the source of DIR inaccuracy is eliminated, and the method is robust to varying crop extent.
A Study of the Impact of Crypto Assets on Portfolio Risk-Return Characteristics Before and After COVID-19 Outbreak (2014–2020)

Following the COVID-19 outbreak, unlike the plunge in traditional mainstream assets, crypto assets have performed very well. Indices that reflect changes in the crypto assets market have also grown in recent years. However, in terms of cryptocurrency index-related studies, there are not many, and the period analyzed is mostly three years. This study analyzes historical data from July 2014 to April 2020 based on the cryptocurrency index CRIX and six other traditional mainstream assets to verify the impact of crypto assets on traditional portfolios. By using the DCC-GARCH model, this study finds out the low dynamic correlation between the crypto assets and traditional ones. Furtherly, by using the mean–variance model, Cornish-Fisher expansion and T-copula CVaR approach to check the frontier line and portfolio performance, this study finds out that crypto assets have the potential to improve the risk-return characteristics of traditional portfolios.
Homomorphic Comparison Methods: Technologies, Challenges, and Opportunities

The use of cloud computing imposes a number of restrictions related to the security and confidentiality of stored and processed data. One of the mechanisms that allows expanding the scope of public cloud services for processing personal and medical data is homomorphic encryption. Homomorphic encryption allows performing arithmetic addition and multiplication operations on encrypted data. To expand the scope of homomorphic encryption, efficient algorithms are needed to implement the operation of comparing encrypted numbers. In this paper we investigate existing algorithms of encrypted numbers comparison for homomorphic encryption schemes operating both with integers and with fixed-precision numbers. We have shown that for homomorphic integer ciphers, there are efficient algorithms for comparing encrypted numbers based on Lagrange interpolation of a function of two variables. We have shown that fixed-precision comparison of encrypted numbers is an open problem.
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment

Many transfer problems require re-using previously optimal decisions for solving new tasks, which suggests the need for learning algorithms that can modify the mechanisms for choosing certain actions independently of those for choosing others. However, there is currently no formalism nor theory for how to achieve this kind of modular credit assignment. To answer this question, we define modular credit assignment as a constraint on minimizing the algorithmic mutual information among feedback signals for different decisions. We introduce what we call the modularity criterion for testing whether a learning algorithm satisfies this constraint by performing causal analysis on the algorithm itself. We generalize the recently proposed societal decision-making framework as a more granular formalism than the Markov decision process to prove that for decision sequences that do not contain cycles, certain single-step temporal difference action-value methods meet this criterion while all policy-gradient methods do not. Empirical evidence suggests that such action-value methods are more sample efficient than policy-gradient methods on transfer problems that require only sparse changes to a sequence of previously optimal decisions.
Digital Skills for Teachers: Policies and Initiatives in Greece

The emergent shift of all education activities to online remote teaching and learning (due to the pandemics) revealed the need of having teachers with advanced digital skills. This paper describes long term policies and initiatives to enhance teachers’ digital skills in Greece. A variety of government authorities are responsible to issue and implement policies for promoting digital skills. Thousands of teachers have been trained in integrating digital technologies in their teaching practice. They are from all disciplines and subjects in primary and secondary education. Furthermore, implemented projects offer a huge number of educational resources to support teachers in their teaching practice.
Generative Adversarial Networks for automatic detection of mounds in Digital Terrain Models (Mars Arabia Terra)

&amp;lt;p&amp;gt;Mounds are positive relief features that can be ascribed to a variety of phenomena; they can be related to monogenic edifices due to spring or mud volcanism, rootless cones on top of lava flows, pingos and so on. In the case of sedimentary or spring case of mud extrusion, these mounds can be widespread regionally and/or contained in large complex craters, often in populations of several hundreds or thousands . Previous work on detection of such mounds in the Mars Arabia Terra involved exploiting morphometric parameters and mapping them onto Digital Terrain Models . In this work, we take a step further and develop more general methods to automatically detect them without explicitly defining the topographical features. We achieve this by using a generative framework trained in an adversarial fashion to produce realistic mappings with only a small number of training samples. Further, we introduce a terrain simulator based on this framework that learns the terrain simulation parameters, and allows us to induce domain specific knowledge automatically into the network.&amp;amp;#160; Our key results indicate that learning latent representations based on simulations can offer improvements in detection accuracy, while making it more robust to changing terrain scenarios.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;&amp;lt;/p&amp;gt;
Motion Object Detection and Tracking Optimization in Autonomous Vehicles in Specific Range with Optimized Deep Neural Network

Autonomous Vehicles have become increasingly popular around the world in recent years. The potential of this technology is clear and transportation is expected to change dramatically over what is known today. The advantages of Autonomous Vehicles are pollution reduction in urban areas due to improved driving and fuel efficiency to help control traffic flow and parking problems. In addition, Autonomous Vehicles accelerate people and cargo transportation, as well as reducing human errors. There are a variety of issues in the field of Autonomous Vehicles which one of them is the issue of detecting and tracking motion objects as obstacles. In this article, we presented a novel method to optimizing motion objects detection and tracking from the KITTI data set in Autonomous Vehicles in a specific range in between 50 to 80 meters. This approach proposes a real-time and simultaneous structure for motion detection and tracking, so that the data fully enter the combined method called CRF-based Deep Spiking Neural Network with Probabilistic Particle Filter (PPF-DSNN). In fact, CRF-based deep spiking neural network is used to train and test data to extract features and probabilistic particle filtering methods with the aim of detecting and tracking these moving objects. The results represent that proposed approach is highly efficient in comparison to recent methods.
Improved Artificial Bee Colony Algorithm Based on Cauchy OBL

Abstract Artificial bee colony algorithm is a competitive swarm optimization algorithm for many optimization problems. However, traditional artificial bee colony algorithms often have the problems of insufficient development performance, easy to fall into local optimization and slow convergence. In order to overcome this defect, this paper introduces Cauchy OBL into artificial bee colony algorithm, and proposes an artificial bee colony algorithm based on Cauchy OBL. In the initialization stage, the algorithm uses the Cauchy OBL group to compete with the traditional initial group, selects excellent individuals to form an improved initial group, and improves the quality and diversity of the population; in the neighborhood search stage, the Cauchy OBL process is added to improve the global exploration ability of the algorithm. At the same time, in order to speed up the convergence speed, the global optimal solution and multi-dimensional update strategy are introduced. The simulation results show that the improved algorithm is easy to jump out of the local optimization and has higher search accuracy and faster convergence speed.
Implementation of the Simple Multi Attribute Ranking Technique Method as a Model for Decision Making in Determining the Talents and Interests of Children in Continuing Education

Abstract Talent and interest are very influential on students in the process of their development and learning. This is because every child needs an education program that matches their talents and interests. Many problems occur due to mistakes of parents or teachers in determining talent and asking students so mistakes in determining education still tend to be incorrect. So students find it difficult to adapt to the education they face because it is not in accordance with their abilities and potential. Therefore, we need a modeling of decision making based on the criteria of talent and interests of children using the Simple Multi Attribute Ranking Technique modeling. The implementation of the SMART method in this study is able to provide solutions to the problem of decision making talents and interests of children. This study uses a sample of 15 student data which is then entered into the computation of the SMART calculation method. The result is that 3 students who were sampled were able to have grades above the standard or scores above 70 points that have been set based on predetermined rules. While the other 12 students have the final result below the standard that has been set which is below the value of 70 points.
Enhancing Multi-Class Classification in One-Versus-One Strategy: A Type of Base Classifier Modification and Weighted Voting Mechanism

An effective way to solve multi-class classification problems is the One-Versus-One (OVO) decomposition strategy. When applying the OVO strategy to practice, there may be a `tie vote' phenomenon so that some samples cannot be classified. This article proposed a weighted voting mechanism formulated by base classifier accuracy, which is simple and effective to eliminate the unclassifiable area. In addition, one can further improve the classification ability of base classifiers by modifying two-class base classifiers to three-class base classifiers. In the simulation experiments, logistic regression and support vector machine are taken as base classifiers, and the white wine dataset and red wine dataset are viewed as the benchmark of four-class and five-class classification respectively. The results indicate that for multi-class classification, higher accuracy and efficiency can be achieved by modifying the basic classifier and implementing the weighted voting mechanism.
Low-complexity Neural Network-based MIMO Detector using Permuted Diagonal Matrix

DNN has achieved state-of-the-art performance in MIMO detection problem. However, the deep and large model is hard to deploy to resource constrained platforms. In this work, we propose to provide a sparse DNN model for MIMO detection using permuted diagonal matrices. As a result, our model is with low complexity and doesn't have indexing overhead like other sparsity method. Experiment shows that our method can achieve high sparsity while maintaining the model performance.
Keyphrase Extraction from Scientific Articles via Extractive Summarization

Automatically extracting keyphrases from scholarly documents leads to a valuable concise representation that humans can understand and machines can process for tasks, such as information retrieval, article clustering and article classification.This paper is concerned with the parts of a scientific article that should be given as input to keyphrase extraction methods.Recent deep learning methods take titles and abstracts as input due to the increased computational complexity in processing long sequences, whereas traditional approaches can also work with full-texts.Titles and abstracts are dense in keyphrases, but often miss important aspects of the articles, while full-texts on the other hand are richer in keyphrases but much noisier.To address this trade-off, we propose the use of extractive summarization models on the full-texts of scholarly documents.Our empirical study on 3 article collections using 3 keyphrase extraction methods shows promising results.
SAGA: A Hybrid Technique to handle Imbalance Data in Software Defect Prediction

Software defect prediction has been a concurrent topic in software quality-based research. Predictive models that identify defect prone parts of Software can be evolved from defect data and software metrics. Various studies conducted in the past have explored Machine Learning-based approaches for this purpose but the problem of handling imbalanced defect data without compromising on the model's performance remains at large. In this work, we have proposed, compared, and analyzed a hybrid technique, SAGA(SMOTE + AdaSS + Genetic Algorithm), for solving the imbalance problem faced in software defect prediction. SAGA employs ensemble classification based on feature space partitioning in conjunction with the Synthetic Minority Oversampling technique. Various parameters related to feature space partitioning are optimized using the Genetic Algorithm. The values of ROC-AUC, G-mean, Balance, and Accuracy obtained on open-source datasets confirm the effectiveness of the proposed technique.
Bootstrapping Multilingual Metadata Extraction: A Showcase in Cyrillic

Applications based on scholarly data are of ever increasing importance.This results in disadvantages for areas where high-quality data and compatible systems are not available, such as non-English publications.To advance the mitigation of this imbalance, we use Cyrillic script publications from the CORE collection to create a high-quality data set for metadata extraction.We utilize our data for training and evaluating sequence labeling models to extract title and author information.Retraining GROBID on our data, we observe significant improvements in terms of precision and recall and achieve even better results with a self developed model.We make our data set covering over 15,000 publications as well as our source code freely available.
Correct Pronunciation Detection for Classical Arabic Phonemes Using Deep Learning

The pronunciation of the Arabic language is required all articulatory phonetics organs to formulate the correct sounds of a word. It is challenging for non-native Arabic speakers to learn to recite the Holy Quran with correct “Tajweed” rules and pronunciation. The limited contributions are made in the development of classic Arabic short vowels dataset that hinder the development of speech recognition system to facilitate the learner during their Holy Quran learning process. Therefore, it is required to have a collection of audio classic Arabic datasets that can help speech recognition and mispronouncing detection of the classic Arabic speech. In this paper, we aim to collect the classical Arabic alphabet with short vowels. Short vowels are an essential part of the Arabic language. One word of the Arabic language consists of at least one or two short vowels. First, we start with requirement gathering for the classical Arabic short vowels. Our primary focus is to record and process the collected audio dataset. The first release of the audio dataset collected consists of 2892 Arabic alphabet short vowels. A significant effort is applied in preprocessing of the dataset consisting of 84 classes of the Arabic alphabet short vowels. Then, the dataset is tested using a sequential convolution neural network (CNN) on 312 phonemes of the collected Arabic Alphabet /a/ “Alif” with short vowels. The result shows that CNN gives high testing accuracy of 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and a loss of 0.27.
Subjective Quality Optimized Efficient Image Compression

In this paper, we propose an efficient image compression framework that is optimized for subjective quality. Our framework is mainly based on the NLAIC (NonLocal Attention Optimized Image Coding) model which applied Variational Autoencoder (VAE) and non-local attention module to end-to-end image compression. This work makes two major contributions to the NLAIC framework. First, our models are optimized for subjective-friendly loss functions rather than conventional MSE (Mean Squared Error) or MS-SSIM (Multiscale Structural Similarity) which was widely used in previous works. Second, we introduce block-based inference mechanism to reduce the running memory consumption of the image compression network, and suggest a partial post-processing step to alleviate block artifacts caused by block-based inference in a lightweight computational fashion. Experiments have proved that the image reconstructed by our method can preserve more texture details than models trained for optimal MSE or MS-SSIM and also present capability for high-throughput decoding.