Reviewerly/exHarmony huggingface dataset:
A Predictive Model of Electricity Resident User Payment Based on Transfer Learning

The development of smart meters and smart terminals has brought new opportunities to the power industry, providing power companies with hundreds of times higher user data than before, and there are more and more studies on these power data. One of the characteristics of power data is that power data is usually very regional or regional. Power data in different geographical locations have certain similarities, but there are obvious differences. If a single model is trained for the entire power data, the differences within the data are ignored, and if different models are trained separately for all regions, the similarity between the data is ignored. Sample-based transfer learning solves this difficult problem. Through the transfer of sample knowledge, data in different domains can assist each other to improve the accuracy of their respective models. This paper proposes a neural network prediction model based on sample-based transfer learning for the probability of payment by electricity residents, which improves the accuracy of prediction and confirms the effectiveness of transfer learning.
Designing an individual educational program activities of undergraduates

Ensuring the subject position of a student, and especially a master’s student, is one of the most pressing problems of modern training of teaching staff. The purpose of the article is to present the experience of designing individual educational activities of undergraduates as a subject-oriented technology. Research methods used: literature analysis, study and generalization of experience, observation, focus group, survey, conversation. The article defnes the concepts of «individual educational activity» (IOD), « designing individual educational activities»; the article considers the essence and principles of designing individual educational activities of undergraduates, describes the main projects of IOD (plan, program, route), highlights and reveals the main stages of designing individual educational activities of undergraduates: preparatory, motivational and diagnostic, understanding and analysis, goal setting and planning, implementation of individual educational projects, evaluation and analysis of achievements, making adjustments to projects of individual educational activities. It shows the use of a number of pedagogical tools that are used in the design of individual educational activities: cluster, portfolio, mapping, tutorial, etc. The conditions that ensure the success of designing individual educational activities of undergraduates are called.
Haze Removal Using Generative Adversarial Network

Sanjay, Amrita Nair, J. Jyothisha Gopakumar, G.The problem of haze removal has been addressed in many computer vision researches. Haze removal is the process of eliminating the degradation present in hazy images and getting the clearer counterpart. The presence of haze distorts the image, and as a result, it will be difficult to apply various image processing techniques on such images. The challenging aspect in haze removal arises due to the lack of depth information in images degraded by haze. The earlier methods for haze removal include various hand-designed priors, usage of the atmospheric scattering model or estimation of the transmission map of the image. The limitation with these models is that they are heavily dependent on the assumption of a good prior. In recent years, various models have been proposed which effectively remove the degradation caused by haze in images using various convolutional neural network architectures. This paper reviews a model which performs haze removal on a single image using generative adversarial network (GAN). The main advantage of this method is that it does not require the transmission map of the image to be explicitly calculated. The model was evaluated using NYU depth dataset and 0-Haze dataset. The model was able to significantly enhance the quality of the images by generating the corresponding haze-free counterpart. The model was evaluated using the peak signal-to-noise ratio and structural similarity index.
Regularized Semi-Supervised Metric Learning with Latent Structure Preserved

Metric learning is a critical problem in classification. Most classifiers are based on a metric, the simplest one is the KNN classifier, whose outcome is directly decided by the given metric. This paper will discuss semi-supervised metric learning. Most traditional semi-supervised metric learning algorithms preserve the local structure of all the samples (including labeled and unlabeled) in the input space, when making the same labeled samples together and separating different labeled samples. In most existing methods, the local structure is calculated by the Euclidean distance which uses all the features. As we all know, high dimensional data lies on a low dimension manifold, and not all the features are discriminative. Thus, in this paper, we try to explore the latent structure of the samples and use the more discriminative features to calculate the local structure. The latent structure is learned by clustering random forest and cast into similarity between samples. Based on the hierarchical structure of the trees and the split function, the similarity is obtained from discriminant features. Experimental results on public data sets show our algorithm outperforms the traditional similar related algorithms.
A Study on the Improvement of Image Classification Performance in the Defense Field through Cost-Sensitive Learning of Imbalanced Data

With the development of deep learning technology, researchers and technicians keep attempting to apply deep learning in various industrial and academic fields, including the defense.Most of these attempts assume that the data are balanced.In reality, since lots of the data are imbalanced, the classifier is not properly built and the model's performance can be low.Therefore, this study proposes cost-sensitive learning as a solution to the imbalance data problem of image classification in the defense field.In the proposed model, cost-sensitive learning is a method of giving a high weight on the cost function of a minority class.The results of cost-sensitive based model shows the test F1-score is higher when cost-sensitive learning is applied than general learning's through 160 experiments using submarine/non-submarine dataset and warship/non-warship dataset.Furthermore, statistical tests are conducted and the results are shown significantly.
Opinions on the Construction of “Mass Entrepreneurship and Innovation + Internationalization” Educational Ecosystem in Rail Transit Higher Vocational Colleges- Take Guangzhou Railway Polytechnic as an Example

Mass entrepreneurship and innovation and the construction of “the Belt and Road Initiative” are closely linked to each other, and vigorously building an educational ecosystem of “mass entrepreneurship and innovation + internationalization” has become the focus of government policy. In response to the needs of mass entrepreneurship and innovation and “high-speed railway going global” strategy in the new era, the ecosystem construction of the rail transit higher vocational colleges needs to reflect the theoretical guidance, cultural connotation and craftsmanship spirit of China's railway characteristics in the new era. To tackle the unsatisfying material conditions, operating mechanism and impetus system, this paper puts forward the three-dimension integrated strategy for the “mass entrepreneurship and innovation + internationalization” educational ecosystem. The three dimensions are theoretical system (theoretical guidance, top-level design, cultural output, industry-education integration), implementation strategy (teaching reform, teachers construction, platform construction and mechanism innovation) and service support (organization and leadership, condition guarantee, three-dimensional guidance and brand promotion), so as to build the educational ecosystem in which mass entrepreneurship and innovation and internationalization integrate with and promote each other.
Special issue on Current trends in research on software agents and agent-based software systems

This special issue of autonomous agents and multi-agent systems sought research articles that discuss relevant topics for the current trends in research on software agents and agent-based software systems. Topics of interest included techniques and technologies to engineer agent-based software systems, as well as languages, frameworks, and infrastructures broadly related to software agents and agent-based software systems.
Light Amplification by Quantum Confinement (LAQC) in quantum optical lithography

A nanoscale coherent light source, operating at room temperature, Light Amplification by Quantum Confinement (LAQC), has been realized during Quantum Optical Lithography studies. A model for LAQC light burst is proposed in which the fluorescent emitters are Tb3+ ions. We have selected experimental conditions for an emission disc with a diameter of 10 nm. Differences between LAQC, superradiance and superfluorescence are discussed.
Horizontal Privacy-Preserving Linear Regression Which is Highly Efficient for Dataset of Low Dimension

Linear regression is a widely used machine learning model for applications such as personalized health-care prediction, recommendation systems, and policy making etc. Nowadays one important trend of applying this model (also others) is privacy-preserving linear regression, in which multiple parties, each possessing a part of dataset, jointly perform the learning process, while paying a specific attention to the goal of preserving privacy of their data. Consequently some works on how to achieve this goal with various properties appear in recent years.
Speckle Reduction in SAR Images using CNN

The importance of remote sensing has ballooned in the recent decade, due to the increased need for its applications, from commercial and economic, to intelligence and military applications. Synthetic Aperture Radar (SAR), an important remote sensing instrument captures data that are inherently affected with speckle noise due to its coherent imaging, which makes working with SAR data complicated. Therefore, Synthetic Aperture Radar (SAR) images can benefit greatly from good speckle reduction or suppression techniques, making them easier to analyze and utilize their maximum potential. This paper gives a technical review of various speckle filtering approaches for Synthetic Aperture Radar Images (SAR), discussing the drawbacks of the older conventional methods, and the improvements the newer convolutional Neural Networks (CNN) based solutions bring to the table.
Robust Secured Framework for Online Business Transactions over Public Network

Today technology has advanced and mature at level that the financial institution such as bank employed the system of banking by mouse click. This is possible through the help of local area network and Internet. Internet is available everywhere. The banking functions of online system makes human life more ease to not physically arrive at bank premise to do transactions. Online banking system poses various security risks. The risks are required to be analyzed and proper prevention and mitigation techniques have been employed by bank to protect the sensitive information following through the local network and also with public network. The various protection mechanisms with tool and technologies are used by bank to protect the sensitive and critical customer and bank information from cyber criminals. The intruder can disrupt the functions of online bank by service congestion or by vulnerability analysis. In this research work we have proposed a layer based security framework for bank that operates its businesses under online mode. The authorization matrix defines the level of functional depth with defined role of each employee of the bank. Bank also protects its information from personnel level abuse by following various measures of functional aspects under the bank.
Deployment and Effects of an App for Tracking and Tracing Contacts During the COVID-19 Crisis

The general idea of tracking and tracing apps is that they track the contacts of users so that in case a user tests positive for COVID-19, all the other users that she has been in contact with get a warning signal that they have potentially been in contact with the COVID-19 virus. This is, to quarantine potential carriers of the virus even before they show symptoms. We set up a scenario in which we test the effects the introduction of such an app has on the dynamics of infection with varying amounts of app users. Running the experiments resulted in a slightly lower peak of infections for higher app usages and the total amount of infected individuals over the course of the whole run decreased not more than 10% in any case. The app seems mainly effective in decreasing contacts and infections in public spaces (except hospitals) while increasing the contacts and infections at home.
VisRel: Media Search at Scale

In this paper, we present VisRel, a deployed large-scale media search system that leverages text understanding, media understanding, and multimodal technologies to deliver a modern multimedia search experience. We share our insight on developing image and video understanding models for content retrieval, training efficient and effective media-to-query relevance models, and refining online and offline metrics to measure the success of one of the largest media search databases in the industry. We summarize our learnings gathered from hundreds of A/B test experiments and describe the most effective technical approaches. The techniques presented in this work have contributed 34% (abs.) improvement to media-to-query relevance and 10% improvement to user engagement. We believe that this work can provide practical solutions and insights for engineers who are interested in applying media understanding technologies to empower multimedia search systems that operate at Facebook scale.
FNAB-Based Prediction of Breast Cancer Category Using Evolutionary Programming Neural Ensemble

The process of diagnosis and survival rate along with endurance period is greatly decided by the type of cancer and the current existing stage of cancer disease. The information that is accurate could provide lots of help during patient treatment management and increase the chance of survival. The biopsy process through fine needle aspiration has generally been applied to rule out the presence of malignancy lesions. Cytologists analyzed the details and tried to establish the correlation among the various observations to deliver the outcome. The limitation of expertise and possibilities of natural human error can cause a significant impact on the diagnosis process as well as patient economical and cognition conditions. In this article, computational intelligence has developed over the neural network platform to predict the possibilities of lesions’ categorical belongings. The feed-forward architecture has considered developing the predictor because of its universal approximation quality. To obtain more robust outcomes in the decision instead of a single classifier, ensembles of classifiers have proposed where every individual classifier is having variability in the training data to avail the quality of knowledge diversity. The formation of weight-oriented ensemble has been done by evolutionary programming. It has been observed that the proposed method has delivered a high value of correct recognition in comparison to individual classifiers. The benefit of the proposed form of the ensemble has compared against conventional methods like majority voting and mean value decision. The strength of ensemble forming capability by evolutionary programming has compared with particle swarm optimization. The proposed ensemble benefit has further been tested over receiver operating characteristics environment to meet the practical challenges of variability in decision threshold value.
Integrating knowledge graph into deep neural network-based recommender system

In recent years, the integration of knowledge graphs into explainable recommendation systems has attracted more and more attention. And the relations existing in the knowledge graph can provide much information while exploring the users' preference. However, existing approaches only consider the single relation between entities, so they lack accuracy and practicality for multiple relations. In addition, previous work cannot capture the semantics of all paths. Towards this end, we propose three significant modelling advances: (1) besides the relations, we also learn to jointly reasoning on the entities and entity-types; (2) we use elaborate pooling layer to incorporate the paths between entities; (3) we take a better way to extract paths' semantic representations. The experimental study demonstrates the superiority of our method compared with the state-of-the-art ones.
Deep Correlation Filters for Robust Visual Tracking

In this paper, we propose novel deep correlation filters (DCFs) model, which is an end-to-end deep convolutional network that simultaneously learns convolutional features, multiple diverse correlation filters, and scale estimation for robust visual tracking. Compared with existing tracking methods based on correlation filters and deep learning, the proposed DCF enjoys several merits. First, it exploits the spatial transformer into the network to discover the image patches that are most relevant to target object. As a result, it can effectively handle scale variation. Second, the spatial transformer and correlation filters can complement and enhance each other for object translation estimation, resulting in robust tracking performance. Third, to the best of our knowledge, the DCF is the first correlation filter based deep tracker achieving object translation and scale estimation jointly. Extensive results on four challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against state-of-the-art trackers.
Diachronic Interpretability &amp;amp; Automated Systems

If a system is interpretable today, why would it not be as interpretable in five or ten years time?Years of societal transformations can negatively impact the interpretability of some machine learning (ML) systems for two types of reasons.These two types of reasons are rooted in a truism: interpretability requires both an interpretable object and a subject capable of interpretation.This object versus subject perspective ties in with distinct rationales for interpretable systems: generalisability and contestability.On the generalisability front, when it comes to ascertaining whether the accuracy of some ML model holds beyond the training data, a variety of transparency and explainability strategies have been put forward.These strategies can make us blind to the fact that what an ML system has learned may produce helpful insights when deployed in real-life contexts this year yet become useless faced with next year's socially transformed cohort.On the contestability front, ethically and legally significant practices presuppose the continuous, uncertain (re)articulation of conflicting values.Without our continued drive to call for better ways of doing things, these discursive practices would wither away.To retain such a collective ability calls for a change in the way we articulate interpretability requirements for systems deployed in ethically and legally significant contexts: we need to build systems whose outputs we are capable of contesting today, as well as in five years' time.This calls for what I call 'ensemble contestability' features.
Visualising Temporal Uncertainty: A Taxonomy and Call for Systematic Evaluation

Increased reliance on data in decision-making has highlighted the importance of conveying uncertainty in data visualisations. Yet developing visualisation techniques that clearly and accurately convey uncertainty in data is an open challenge across a variety of fields. This is especially the case when visualising temporal uncertainty. To facilitate the development of innovative and accessible temporal uncertainty visualisation techniques and respond to an identified gap in the literature, we propose the first-ever survey of over 50 temporal uncertainty visualisation techniques deployed in numerous fields. Our paper offers two contributions. First, we propose a novel taxonomy to be applied when classifying temporal uncertainty visualisation techniques. This takes into account the visualisation's intended audience, as well as its level of discreteness in representing uncertainty. Second, we urge researchers and practitioners to use a greater variety of visualisations which differ in terms of their discreteness. In doing so, we believe that a more robust evaluation of visualisation techniques can be achieved.
Accurate Answers Selection and Expert Recommendation in Community Question Answers System

Question answering framework is further insensitive for the user. These days, user ask the question on the web and accordingly they will get the answer for that question, since perusing is the primary requirement for every individual. By measuring the user request, the system will grant with answer. However, the calculation time has increased by just holding up the time and same sort of inquiries posed by various user, where system requires to offer parallel responses regularly to various users. To stay away from this, `PLANE' technique is proposed to tentatively rank the candidates extracted from the suitable question pool. When user ask few questions, at that point system offer answers for that question in positioning structure and the system recommends most conspicuous evaluation answer to the user. Heceforth, this research work proposes the expert recommendation system to provide suitable answer for the question, which is polled through the user and in addition the proposed research work set up a regular circumstance sentence level clustering strategy in which a single question can get multiple answers. Mainly, the system provides appropriate answer to the question raised by the user.
Analysis of Traffic Based on Signals Using Different Feature Inputs

The Regional Transportation Commission of Southern Nevada (RTC) manages vehicle traffic in Clark County, Nevada. The RTC division that manages the roadway and freeway street signal devices is the division of Freeway Arterial System of Transportation (FAST). They capture and store their maintenance vehicle trucks’ GPS trip information into a publicly available data set. This data set brings a tremendous opportunity to analyze the traffic trends. This study uses machine learning algorithms to solve two types of problems, classification and regression. For classification, we have obtained 65% accuracy using signal types as the target and significantly low accuracy when we use the total stop time as the target variable. For regression, both Random Forest and Support Vector models performed poorly. However, the Random Forest model performed slightly better than the Support Vector model. Altogether these results help us determine the next steps to explore.
An Efficient Cross-Lingual BERT Model for Text Classification and Named Entity Extraction in Multilingual Dataset

In recent times, with the rise of the internet, everyone is being bombarded with tons of information and data from various sources like websites, blogs and articles, social media posts and comments, e-news portals etc. Now all these data are mostly unstructured. In this paper, the authors have tried to explore the efficiency of the cross-lingual BERT model i.e. M-BERT for text classification and named entity extraction on multilingual data. The authors have used datasets of three different languages namely: French, German and Portuguese to evaluate the model performance.