Reviewerly/exHarmony huggingface dataset:
Software Change Prediction using Ensemble Learning on Object Oriented Metrics

In order to reduce developers’ time testing the defects in software, this research work has come up with an ensemble learning approach using various machine learning and deep learning classifiers. This empirical study starts with the data collection process. Own dataset has been created by using Understand tool and further it will be applied over sampling techniques. The data set has a binary class output, and thus by applying data over sampling techniques, the number of instances of both classes remain comparable. After that various classifiers are trained with the entire training data set. After this step, the proposed model will predict the outcome that these classifiers would create using the training set and compare them with the actual values of the training dependent variable. After this the information that claim which of the classifiers were able to correctly predict the outcome will be given a data point. After that the neural networks will be trained for each of the classifiers with input variables as training independent variables and output variables as the previously recorded data as to whether a classifier correctly classifies a data point or not. This predicting network would tell us whether the classifier corresponding to that particular network would correctly classify a particular data point or not. To predict the outcome of any new data point, it will be fed to the predicting network and find which classifier would correctly predict the outcome and the aggregated result of the selected classifiers would be reported.
Reformatted contrastive learning for image classification via attention mechanism and self-distillation

Abstract Image classification, a basic task for computer vision, is widely achieved using deep learning methods. Recently proposed contrastive learning has made a great progress to feature representation in self-supervised manner, which outperforms traditional contrastive loss, for instance, triplet loss and N-paired loss. In this paper, we propose a reformatted contrastive learning paradigm which is an extension of conventional self-supervised contrast learning. Specifically, multiple positive and negative pairs are constructed for each anchor, thus creating a link between the positive and negative factors. In addition to this, we employ a self-attention module in the network architecture with the aim of focusing on foreground pixels and building long-range dependencies across regions. We also present a self-distillation approach for fine-tuning the classifier using a well-trained feature encoder. which improves the generalization ability of our method. Experiments on CIFAR100 and CUB200-2011 reveal that our method outperforms rival methods, confirming the efficacy of our strategy.
Weather Variation and Accuracy of Weather Prediction During Dry Season 2021 in The Jabotabek Using Voluntary Weather Observation Based on Social Media

Abstract The dynamics of rain events in Indonesia are very high, which causes variations in the accuracy of predictions and in situ rainfall observations or remote sensing rainfall estimates. Observations of the several extreme weather events show that rain fall occurs unevenly, especially in the city areas of Jakarta, Bogor, Tangerang, and Bekasi, commonly abbreviated as the Jabotabek. This region is a vulnerable area to hydro-meteorological disasters and evaluate how the influence of rain variations on the accuracy of weather prediction is constrained by the scarcity of in situ precipitation data. Then, the purpose of this study was to promote voluntary weather observations and to get an overview of the variation in the incidence of rain in the Jabotabek area through the STMKG Weather Care program. This team designed form of observation as easy as possible for respondents to understand, simple, and not time-consuming and spreaded using Google Forms and disseminate via WhatsApp as the most popular social media today. The results of the form filling indicate that social media has the opportunity to be developed to support the collection of weather data. Based on the evaluation of the form filling, it shows that the form is sufficient so that the respondents make relatively few mistakes. Moreover, the respondent’s error is generally in filling out the district’s name. The results of this voluntary weather observation can be used for the analysis of weather variations and the evaluation of weather predictions. The results of the analysis show that the higher the variation in weather conditions, the higher the difficulty of prediction, as the Bogor area has an accuracy of only 25%. Meanwhile, places, where the weather tends to be homogeneous, have an effect with high accuracies, such as in Bekasi Regency, which reaches 85%.
Research on Construction of Dispatching Knowledge Model of Power Grid

With the construction of the new power market, the tasks of dispatching operation in the power grid become more frequent, and the scenes of dispatching operation are more complicated. The existing dispatching operation system and the technology of decision in power grid cannot meet the requirements. Therefore, this work establishes a knowledge model of power grid dispatching to provide an efficient decision-making tool. This work extracts the benefits of historical operation scenarios and the regulation of dispatching of power grid, and establishes a joint knowledge model of power grid dispatching based on mining and extracting the benefits of historical operation scenarios and the regulation of dispatching of power grid by big data analysis technology and natural language processing technology. The joint knowledge model is essential for dispatcher to formulate more reasonable dispatching operations, reduce the difficulty of decision-making and lower the requirements for dispatchers, which ensures the safe operation of the power grid.
Recommendation-based Security Model for Ubiquitous system using Deep learning Technique

Ubiquitous environment embedded with artificial intelligent consist of heterogenous smart devices communicating each other in several context for the computation of requirements. In such environment the trust among the smart users have taken as the challenge to provide the secure environment during the communication in the ubiquitous region. To provide the secure trusted environment for the users of ubiquitous system proposed approach aims to extract behavior of smart invisible entities by retrieving their behavior of communication in the network and applying the recommendation-based filters using Deep learning (RBF-DL). The proposed model adopts deep learning-based classifier to classify the unfair recommendation with fair ones to have a trustworthy ubiquitous system. The capability of proposed model is analyzed and validated by considering different attacks and additional feature of instances in comparison with generic recommendation systems.
Design of the high-dimensional SPKF algorithm and application

In the framework of Kalman filtering, the key points is the estimation of the covariance of prediction error which is used to calculate the Kalman filter gain. For the traditional algorithm, e.g. extended Kalman filter, the priori information is obtained on the basis of the Taylor expansion approximation of the nonlinear system. Because the linear propagation approximation of prior information cannot truly reflect the evolution of the probability distribution of the prediction errors in nonlinear systems, however, the traditional method affects the estimation accuracy of the extended Kalman filter. To solve these problems and be easy to apply to high-dimensional nonlinear systems, this paper improves the SPKF algorithm. Firstly, the prediction error propagation model is re-established to eliminate the approximation error of Taylor expansion in the derivation of the probability density function of the prediction error. Then, a Fisher matrix calculation method that is easier for high-dimensional system is obtained by defining the calculation method of Hilbert inner product of vector and matrix function. To illustrate the merits of the novel algorithm, this paper took the oxygen generation system by using electrolytic water in space station as a case study and conducted the state estimation by respectively using the extended Kalman filter, unscented Kalman filter and high-dimensional stochastic projection Kalman filter. The simulation results shown that the new algorithm can obtain higher estimation precision than the traditional algorithm.
A Model-driven Deep Learning Signal Processing Scheme for OFDM System

Deep learning (DL) has received more attention in physical layer communication. Channel estimation (CE) and signal detection (SD) constitute the key modules within the signal processing chain of orthogonal frequency-division multiplexing (OFDM) communication receivers. In this paper, we propose a model-driven deep learning scheme for the OFDM receiver, known as CSNet, which contains two modules, CE module and SD module. This structure maintains the block by block signal processing form in the OFDM system. In addition, we introduce the traditional linear algorithms minimum mean-squared error (LMMSE) and zero-forcing (ZF) to initialize the neural network of two modules, respectively, which improve the training speed of the sub-network and the generalization of the model. Compared with the traditional scheme and data-driven deep learning scheme, the model-driven DL receiver provides a more accurate channel estimation property. Simulation experiments demonstrate that the proposed scheme is robust in bit error ratio (BER) performance especially in the nonlinear case.
Neural texture transfer assisted video coding with adaptive up-sampling

Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to -19.1% and -26.5%, respectively.
GuardiaNN

As more and more mobile/embedded applications employ Deep Neural Networks (DNNs) involving sensitive user data, mobile/embedded devices must provide a highly secure DNN execution environment to prevent privacy leaks. Aimed at securing DNN data, recent studies execute part of a DNN in a trusted execution environment (e.g., TrustZone) to isolate DNN execution from the other processes; however, as the trusted execution environments for mobile/embedded devices provide limited memory protection, DNN data remain unencrypted in DRAM and become vulnerable to physical attacks. The devices can prevent the physical attacks by keeping DNN data encrypted in DRAM; when DNN data get referenced during DNN execution, they get loaded to the SRAM and get decrypted by a CPU core. Unfortunately, using the SRAM with demand paging greatly increases DNN execution time due to the inefficient use of the SRAM and the high CPU consumption of data encryption/decryption.
Solving Multi-Task Optimization Problems using the Sine Cosine Algorithm

Optimization problems relate to the problem of finding minimum or maximum values from a large pools of solutions whereby exhaustive search is practically impossible. Often, optimization problems are solved using metaheuristic algorithms which provide good enough solution within reasonable execution time and limited resources. Recently, much research focus in the literature is devoted on a new kind of optimization problem, called multi-task optimization (MTO). This paper highlights our on-going work dealing with MTO problem. More precisely, our work investigates the adoption of partitioned population based on Sine Cosine algorithm for dealing with MTO problem. We took the team formation problem from IMDB dataset as our case study based on two objectives, minimizing team costs and team load distribution.
NATURISTA: Online Herbal Care WebApplication

Natural products and traditional therapies are quite valuable. The use of plants to treat disease and improve overall health and well-being is known as herbal medicine. Many consumers mistakenly believe that products branded &#x201C;natural&#x201D; are necessarily safe and healthy. Ayurvedic goods are becoming increasingly popular these days. Our idea intends to create a marketplace for herbal products. The major goal of this project is to make it simple for the user to find herbs that will help with the health problem or sickness that he or she is experiencing. This project assists users in curing disease by recommending a list of herbal medications that they should take in order to recover from their condition or ailment. By allowing the user to enter their health concern and search accordingly, this approach greatly reduces the amount of time spent searching. This system also allows users to place orders, which will add things to their shopping cart and allow them to pay for them. This project will be an e-commerce application that will help the dealer, who will act as a mediator between the buyer and the seller, grow their business.
The boundedly rational employee: Security economics for behaviour intervention support in organizations1

Security policy-makers (influencers) in an organization set security policies that embody intended behaviours for employees (as decision-makers) to follow. Decision-makers then face choices, where this is not simply a binary decision of whether to comply or not, but also how to approach compliance and secure working alongside other workplace pressures, and limited resources for identifying optimal security-related choices. Conflict arises because of information asymmetries present in the relationship, where influencers and decision-makers both consider costs, gains, and losses in ways which are not necessarily aligned. With the need to promote ‘good enough’ decisions about security-related behaviours under such constraints, we hypothesize that actions to resolve this misalignment can benefit from constructs from both traditional economics and behavioural economics. Here we demonstrate how current approaches to security behaviour provisioning in organizations mirror rational-agent economics, even where behavioural economics is embodied in the promotion of individual security behaviours. We develop and present a framework to accommodate bounded security decision-making, within an ongoing programme of behaviours which must be provisioned for and supported. Our four stage plan to Capture, Adapt, Realign, and Enable behaviour choices provides guidance for security managers, focusing on a more effective response to the uncertainty associated with security behaviour in organizations.
Speech Assistant System for Anesthesia Surgery Based on Intelligent Decision Algorithm

Nowadays, most of the medical equipment used in anesthesia surgery only provide display and monitoring service for certain types of patient's physiological indicator. It cannot make intelligent decisions for doctors, nor does it have voice reminders. Anesthesia surgery is complex and diverse, involving all kinds of patients. There are many sudden, urgent, and uncontrollable factors in the operation process. It is easy for doctor to ignore patient's physiological indicators and potentially suspicious symptoms due to attention limitation, which can easily lead to medical accidents. In order to solve above problems, we propose a speech assistant system for anesthesia surgery based on intelligent decision algorithm. It firstly realizes data fusion of multiple types of physiological indicators from different monitoring equipment, then uses intelligent decision algorithm to output the fused data as decision-making suggestions, and finally completes human-machine interaction with doctors in form of speech.
Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity

This paper addresses the design of an exponential function-based learning law for artificial neural networks (ANNs) with continuous dynamics. The ANN structure is used to obtain a non-parametric model of systems with uncertainties, which are described by a set of nonlinear ordinary differential equations. Two novel adaptive algorithms with predefined exponential convergence rate adjust the weights of the ANN. The first algorithm includes an adaptive gain depending on the identification error which accelerated the convergence of the weights and promotes a faster convergence between the states of the uncertain system and the trajectories of the neural identifier. The second approach uses a time-dependent sigmoidal gain that forces the convergence of the identification error to an invariant set characterized by an ellipsoid. The generalized volume of this ellipsoid depends on the upper bounds of uncertainties, perturbations and modeling errors. The application of the invariant ellipsoid method yields to obtain an algorithm to reduce the volume of the convergence region for the identification error. Both adaptive algorithms are derived from the application of a non-standard exponential dependent function and an associated controlled Lyapunov function. Numerical examples demonstrate the improvements enforced by the algorithms introduced in this study by comparing the convergence settings concerning classical schemes with non-exponential continuous learning methods. The proposed identifiers overcome the results of the classical identifier achieving a faster convergence to an invariant set of smaller dimensions.
CONDITIONS OF FORMATION OF PEDAGOGICAL CULTURE TEACHERS OF THE MODERN SCHOOL

The article deals with the problem of formation of conditions pedagogical culture of the teachers. It is offered to consider development of education in a context of pedagogical culture.
NLP Based Review Categorization: A Survey

User reviews on social media were quickly gaining interest in the use of emotional analysis which serves as a response to government, public and private companies. Text Mining has various applications like emotional analysis, spam detection, humor detection and news sharing. The rapid growth of unorganized mountains of text data accompanied by an increase in analytics tools opens up great opportunities and challenges for mining research. Automatic labeling of text data is difficult because people tend to express ideas in complex ways. In addition, Emotional data sets are often very sensitive to the domain and difficult to perform analysis on it because emotions like feelings, attitudes and ideas are often full of sayings, onomatopoeia, synonyms, phonemes, symbols and abbreviations. This paper surveys the major contributions made by previous researchers in this area by using Classical machine learning, LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit), RNN (Recurrent Neural Network), Ontology Learning, Summarization, Hybrid Models etc. Then this research work proposes a deep neural network model using Long-Short-Term Memory (LSTM) with word embedding features. The design includes sub-systems such as pre-processing, train-test splitting of the dataset, feature extraction using Word2Vec model and Deep Neural Network. The proposed model will be evaluated using several different metrics such as accuracy, precision, recall and F1-Score.
Higher Education Evaluation Model Based on Multivariate Statistical Analysis from the Perspective of Ecosystem

Higher education is an important part of social development and progress. However, the shortage of higher education resources and the lack of education investment always exist. The ever expanding demand for education makes the education resources which are not abundant more limited. The quality of higher education evaluation plays a very important role in the rapid and stable development of education. At present, the research methods of higher education evaluation are mostly based on qualitative analysis. In the quantitative analysis and research, the health and sustainable development of higher education are often ignored at the target level, and the correlation of various factors in the system is ignored, which has an adverse impact on the evaluation of higher education work and the formulation of reasonable higher education development strategy in the future. This paper applies the idea of multivariate statistical analysis flexibly and efficiently, and establishes a factor analysis model of higher education evaluation based on the perspective of ecosystem under the support of the concept of sustainable development of ecosystem, so as to systematize the application of multivariate statistical analysis in higher education evaluation. The health and sustainability of 16 countries were studied by using the model. Finally, we made a comprehensive evaluation and ranking of them. On the basis of factor stratification, the four common factor modules of comprehensive evaluation are analyzed in detail, which provides a direction basis for the development of follow-up higher education in relevant countries.
Dual Context Based Network for Clothing Parsing

As a branch of semantic segmentation, clothing parsing is of great research value and can be applied in many practical aspects. In this paper, we propose a novel clothing parsing network, which aggregates dual context information to augment pixel representation. Specifically, we extend the FCN with Attention Class Feature Module (ACFM) and Pixel Correlation Module (PCM) to capture class-level context and global context respectively. ACFM is used to capture class-level context through a coarse-to-fine segmentation structure, which describes the average features of each class on the image. The attention mechanism is utilized in ACFM so that the network only needs to focus on the categories which present in the image, and obtains attention class feature which aggregates class-level context. Furthermore, we use PCM to refine attention class feature and obtain an augmented pixel representation that aggregates dual context information by computing pixel correlation matrix which describes the similarity of any two pixels and contains rich global context information. The experiments on the CFPD dataset show that our method achieves 93.03&#x0025; of PA and 50.95&#x0025; of mIoU which is a promising result compared with other state-of-the-art methods.