Reviewerly/exHarmony huggingface dataset:
Pay Attention to Deep Feature Fusion in Crowd Density Estimation

Crowd density estimation has important practical significance for effectively suppressing the occurrence of stampede accidents. However, the crowd counting task can be easily interfered by various factors such as perspective, congestion, occlusion, density, etc., which makes accurate crowd counting a challenging task. To solve these problems, in this paper, we propose an effective hierarchical aggregation module to fuse different scale information in the network. Since the crowd counting task is seriously interfered by the surrounding environment, in this paper we propose to use attention mechanism module to weight the spatial position of the network learned feature map to effectively limit the interference of the background region to the crowd counting task. Finally, a large number of related experiments show that our model in this paper has strong generalization ability while having better performance on several public datasets compared to existing model algorithms.
Evaluation of Cloud Model for Public Transport Service Quality

Abstract In order to practice bus city planning, it is of great significance to construct a reasonable urban bus service system. At present, there are various research models and methods, and a city bus service quality evaluation model is established based on the advantages and disadvantages of various methods. Analytic hierarchy process and coefficient of variation method were combined to form subjective and objective weights, and cloud model was established for comprehensive quality evaluation. The feasibility of the model is verified by the measured indexes of public transportation in Luoyang City, which proves the rationality of the evaluation method and provides reference for other cities.
Tensor Linear Regression and Its Application to Color Face Recognition

Linear regression has achieved the promising preliminary results for face classification. But, most existing methods are incapable of tackling color images classification. The major reason is that they need to transform each color image to a vector or matrix, leading to the loss of multidimensional structure information embedded in color images. To address this problem, we study the tensor linear regression problem, and develop a novel tensor low-rank method, which utilizes tensor-Singular Value Decomposition (t-SVD) based tensor nuclear norm to emphasize the spatial structure embedded in color images. Applying it to color face classification, extensive experiments on three datasets demonstrate that our method is superior to several state-of-the-art methods.
Enhancing Data Protection Provided by VPN Connections over Open WiFi Networks

Open Wi-Fi networks are a serious challenge to sensitive and private data because it is hard to know who else is using the network and monitoring traffic. Such open, free and unencrypted networks might allow an adversary to hack devices connected to them making the use of such networks highly risky and harmful. In order to use these public networks securly, it is recommended to use VPN in Tunneling Mode to assure that the data is encrypted during transmission. But this is not enough as most of today's smart devices and laptops run applications that might start communicating with their servers before this VPN has been established. In this work, we solve this problem by creating a device that enables users to access the internet securely over Public Wi-Fi networks and provides security right from the beginning when deployed between the public Wi-Fi and the user's personal devices. Experiments show the security advantages of our solution.
Application of information parameters for the classification of Russian-language texts

The aim of the study is to develop a methodology for the classification of Russian-language texts. This article presents the results of assessing the applicability of information parameters for the recognition of text genres. In this work we used the results published in articles devoted to the study of the relationship of order and chaos in discrete systems. The work is performed in the department of information technologies, IRIT, UrFU
Spherical Light-Weight Data Hiding in 360-Degree Videos With Equirectangular Projection

In this paper, we propose a spherical light-weight data hiding technique for 360-degree videos with equirectangular projection between sphere and plane. In particular, computationally efficient least significant bit (LSB) data hiding is applied to the color encoded equirectangular projection of the sphere. As viewers put more attention to the areas around the equator of a 360-degree video compared to the poles, LSB data hiding may be performed in the regions around the poles without causing perceptually significant quality degradation. In addition, the equirectangular projection induces a huge amount of data redundancy in the areas near the poles which increases the capacity available for LSB data hiding. A performance assessment of the proposed spherical light-weight data hiding technique is conducted using the weighted-to-spherically-uniform peak-signal-to-noise ratio (WS-PSNR) and the Craster parabolic projection PSNR (CPP-PSNR). Because both metrics take the non-linear relationship between samples on the sphere and the samples mapped to the plane into account, they are well suited for assessing the performance of the spherical LSB data hiding technique. Numerical results are provided for scenarios in which a 360-degree cover video carries a secret video using different numbers of bit planes. It is shown that video fidelity in terms of WS-PSNR and CPP-PSNR is indeed kept high in the 360-degree stego-video as long as the LSB data hiding is performed in the areas around the poles.
Design and evaluation of a dynamic job search website for non-literate users of Pakistan

Among the many available online job searching solutions, very limited are proposed for non-literate users. The available solutions have tested the possibilities using static web pages which are not applicable in the real world for non-literate users because of the dynamism of the real world. In this research, a dynamic job search solution is presented for non-literate users of Pakistan. User Interface (UI) requirements were identified through an extensive literature review. The culturally specific content in the form of hand-drawn sketches, voice feedback, semi-illustrations was identified and incorporated in the job seekers (non-literate) module. The same module was evaluated by the non-literate and semi-literate participants in a field study as per the usability specification defined by ISO-9241-11. The results have shown that non-literate users could search for a job efficiently in the minimum task completion time as compared to the time reported in the previous literature. They were satisfied and motivated to use the website again to search for jobs. The results indicate a potential for designing ICT-based solutions to provide services to the non-literate users in developing countries.
Support Matching: A Novel Regularization to Escape from Mode Collapse in GANs

Generative adversarial network (GAN) is an implicit generative model known for its ability to generate sharp images. However, it is poor at generating diverse data, which refers to the mode collapse problem. It turns out that GAN is prone to emphasizing the quality of samples but ignoring their diversity. When mode collapse happens, the support of the generated data distribution is not aligned with that of the real data distribution. We thus propose Support Regularized-GAN (SR-GAN) to address such a mode collapse issue by matching their support. Our experiments on synthetic and real-world datasets show that our regularization can mitigate the mode collapse and also improve the data quality.
Adversarial Domain Adaptation Network for Semantic Role Classification

In this paper, we study domain adaptation of semantic role classification. Most systems utilize the supervised method for semantic role classification. But, these methods often suffer severe performance drops on out-of-domain test data. The reason for the performance drops is that there are giant feature differences between source and target domain. This paper proposes a framework called Adversarial Domain Adaption Network (ADAN) to relieve domain adaption of semantic role classification. The idea behind our method is that the proposed framework can derive domain-invariant features via adversarial learning and narrow down the gap between source and target feature space. To evaluate our method, we conduct experiments on English portion in the CoNLL 2009 shared task. Experimental results show that our method can largely reduce the performance drop on out-of-domain test data.
Modeling Distributed Stream Processing Systems Under Heavy Workload

Big data applications play a significant role in diverse fields. Distributed Stream Processing Engines (DSPEs) are widely used to support real time applications efficiently. Partitioning algorithms are used to partition data streams into multiple nodes to process in parallel to gain efficient performance. Aggregation cost is an important factor when process stateful streaming applications using such partitioning algorithms because it plays an important role on performance when final result is being produced in stateful streaming applications. However, impact of aggregation cost in stream processing is not discussed comprehensively in existing literature. We use performance modeling to identify the importance of aggregation cost when workload is high. We implement performance model on a multi-node cluster to predict the same behavior as on single resource performance model. We demonstrate that stateful streaming applications need more resources as compare to stateless applications when workload is high and both stateful and stateless applications are running in the same DSPE. Experiments results show that a stateful streaming application needs more resources compared to a stateless streaming application when both applications are running on the same DSPE when the workload is high. Further experiment results show that the performance modeling may be helpful to predict maximum workload that can be process on a DSPE and increase in parallelism level is not guaranteed to increase the performance of streaming applications.
A New Modified Version of Standard RSA Cryptography Algorithm

Data security is an open researchDas Sudhansu Bala challenge due to the rapid evolution in communication over an unsecured network. In this direction, cryptographic algorithms play an important role in providing the data security against maliciousMishra Sugyan Kumar attacks. This paper elaborates a new asymmetric cryptography approach, i.e. modified RSA algorithm. This proposed method reduces complex calculation involved in RSA algorithm. A novel algorithm has beenSahu Anup Kumar proposed for finding the value of public key (k) and private key (l). Also, a newly derived formula is used to find the ciphertext (N) and plaintext (M). We have explored some major research challenges in the field of RSA algorithm.
Analysis of Electronic Health Records to Identify the Patient’s Treatment Lines: Challenges and Opportunities

The automatic reconstruction of the patient’s treatment lines from their Electronic Health Records (EHRs) is a significant step towards improving the quality and the safety of the healthcare deliveries. With the recent rapid increase in the adaption of EHRs and the rapid development of computational science, we can discover new insights from the information stored in EHRs. However, this is still a challenging task, being unstructured data analysis one of them. In this paper, we focus on the most common challenges for reconstructing the patient’s treatment lines, which are the Named Entity Recognition (NER), temporal relation identification and the integration of structured results. We introduce our Natural Language Processing (NLP) framework, which deals with the aforementioned challenges. In addition, we focus on a real use case of patients, suffering from lung cancer to extract patterns associated with the treatment of the disease that can help clinicians to analyze toxicities and patterns depending on the lines of treatments given to the patient.
An RBF Network Based Crossover for Pseudo-Boolean Optimization

Recombination is an important operator in population metaheuristics. Recently, there has been interest in developing efficient recombination operators that explore characteristics of the optimization problem. We propose using a radial basis function network (RBFN) to generate recombination masks based on information contained in the parents and on past successful recombinations. The RBFN is trained online and the training set is built by adding examples of successful recombinations obtained during the optimization process. The time complexity for training the RBFN is O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), while the time complexity for applying the proposed crossover operator is O(N), the same time complexity of traditional crossover operators. Experiments with NK landscapes indicated that a genetic algorithm (GA) with the proposed crossover was capable of generating better results for successful recombination and improvement rates, when compared to a GA with traditional crossover. However, this did not imply in better results for the best fitness.
Methods of multi-dimensional statistical analysis for assessing the impact of applying technology of paint formulation

Application information of the method of canonical correlations to assess the influence of technological factors on the quality of paint coat of building products and structures are provided below. The algorithm for calculating the method of canonical correlations is considered. It is shown that the substrate porosity is determinative for the paint coat quality. The viscosity of the paint applied by the pneumatic method affects the quality indicators less, especially compare to brush application. The coefficients in canonical variables characterize the strength of the influence of the relevant signs-factors and the efficiency of indicators on the level of communication between them. Various ways of applying paint are considered on substrate with a porosity of 24%, 28%, 32% on the example of oil paints MA-15, of alkyd paint PF-115, of water dispersion paint AK-111, it is shown, that the porosity of the substrate is a decisive factor in determining the quality of the paint coating. The viscosity of the paint when applied by the pneumatic method affects the quality indicators less compared to the brush application.
An Empirical Evaluation of Two Novel Linkage Criteria for Hierarchical Agglomerative Clustering

Hierarchical agglomerative clustering (HAC) is among the most widely adopted algorithms in unsupervised learning. The choice of linkage criteria highly influences the results of HAC. This paper presents and evaluates two novel linkage criteria for HAC. The evaluation is performed on 12 datasets from the literature. Novel criteria are compared to three reference methods from the literature: single linkage, complete linkage, and average linkage.
WorldSkills International and Its Place in Higher Education System

The article discusses the results of the impact of WorldSkills International Movement on the development of national higher education. The authors analyze the main stages of its development, the results of the implementation of WorldSkills standards in the Russian higher education system, dwell on the procedure for the formation of the system of competences that increase the competitiveness of modern specialists. The article includes algorithms for conducting demonstration WorldSkills standards exam, and makes the case for integrated studies to identify trends in the formation and development of professionalism in various activities, develop models of successful specialist, as well as use diagnostic technology to assess his/her personal and professional qualities. In addition, the authors show the relevance of educational innovation through the adaptation of WorldSkills system in conjunction with the modification of training system, and multiplying a promising experience and transfer it to different occupations.
An Experimental Study on the Behaviour of Inconsistency Measures

We apply a selection of 19 inconsistency measures from the literature on artificially generated knowledge bases and study the distribution of their values and their pairwise correlation. This study augments previous analytical evaluations on the expressivity and the pairwise incompatibility of these measures and our findings show that (1) many measures assign only few distinct values to many different knowledge bases, and (2) many measures, although founded on different theoretical concepts, correlate significantly.
Robust design of web services supporting the home administration of drug infusion in pediatric oncology

Cancer home care is a sector of particular relevance for the Italian health service. The budget ceilings imposed by public finance pose the need to reduce hospitalisation costs, moving patients from treatment to home care as much as possible. This is especially true in the field of pediatric oncology, where a protected family environment offers a variety of benefits to young patients and their families. However, in order to guarantee adequate assistance services, an integrated information system for management must be devised as the center of gravity of a coordinated network of, even heterogeneous, actors. This paper focuses on the identification and evolving development of web services for regulation and control of home administration of drug infusion in pediatric oncology. The Service Oriented Architecture (SOA) remains the software architecture of reference, where as the methodological approach is open to a reconsideration of the continuous improvement of the whole process in light of the ceaseless progress of medical science and information technology. In this paper, his goal is achieved by using a methodological design approach that combines the UML modeling based on Case Stories and the process optimization of Axiomatic Design. Case Stories allow the formalization of end users' requirements in UML language, easily interpreted by software developers. On the other hand, Axiomatic Design allows optimizing the design process by identifying the most robust solutions in terms of greater logical coherence and lesser systemic complexity. We analyze a case study focused on the design of web services supporting the home administration of drug infusion, for young cancer patients in home care. Then, we show how the UML-modeling methodology can be used to design new services on the basis of specific Use Cases. Finally, through theAxiomatic design approach we verify the proposed solutions, by identifying the robust set of web services to be implemented.
Lexi-Augmenter: Lexicon-Based Model for Tweets Sentiment Analysis

Sentiment analysis or opinion mining seeks to automate the detection of subjectiveness underlying a text. It is essential for many applications, such as political campaign, online marketing, and products reviews. In the past decade, a line of researchers has studied the problem of automating sentiment analysis. The research in this area falls mainly into two directions: 1) classification models that optimize algorithms and features to predict the polarity of a text, and 2) lexicon-based models that utilize lexicons and rule-based approaches to determine the sentiment of a given text. In this paper, we present a system that constructs lexicon by utilizing both dictionary and context-based approaches and 2,25 million tweets collected from Twitter. The dictionary-based approach finds semantically associated keywords; however, this approach may not detect contextually related keywords. Thus, we apply context-based expansion utilizing neural networks and word-embeddings to find both syntactically and semantically related keywords within corpora without drift. Experimental results suggest that the proposed model yields better lexicons and outperforms the baseline model.
Forecasting the Prices of Cryptocurrencies Using GM(1,1) Rolling Model

Although cryptocurrencies initially emerged as a transnational payment instrument, it has become an investment tool by attracting the attention of investors within the functioning of the capitalist system. In this chapter, the use of cryptocurrencies as an investment tool rather than in commercial transactions is discussed. As is known, most investors remain in a dilemma between the risk of risk aversion and the maximization of returns. Investors in this dilemma try to predict the future price or returns of the financial instruments through various analyzes and thus make an effort to give direction to their investments. These analyses are generally carried out by analyzing the past values of prices or returns by adopting a technical analysis approach. However, since the cryptocurrencies are a relatively new investment tool, it is not possible to reach the previous period price and yield information for an extended period. For this reason, the scope of the chapter is to explain the functioning of the cryptocurrencies as an investment tool in the market and to share information about the types of investors who have transferred their funds to cryptocurrencies by providing statistical information. Then, it is aimed to share the theoretical knowledge about GM(1,1) Rolling Model which has been proved by the literature in which it produces successful results especially in forecasting problems in uncertainty environment. Finally, the price forecasting of popular cryptocurrencies which are Bitcoin, Ethereum, Litecoin, and Ripple was made using the GM(1,1) Rolling Model, and it was tested whether this model is advisable for price forecasting of cryptocurrencies. Results of the Model show that the forecasting errors ranged from 1.35% to 7.76% for 10-days period. Also, direction forecasting results are between 40% and 50% in the same period. Also, returns of the bitcoin investment which made by trusting the results are ranged from −0.60% to −8.18. The results may be considered that the model was successful in forecasting the prices but unsuccessful in the direction forecasting. Even though the estimates are made with low percentages, the time series analyzes made with the lagged data of Bitcoin prices are not successful. Therefore, the technical analysis approach can be interpreted as not sufficient for modeling Bitcoin prices. So, these results show that defining bitcoin price movements is not only a forecasting problem but also a classification problem.
GAIM: A C++ library for Genetic Algorithms and Island Models

The need for optimization is almost everywhere today, especially with the rise of deep learning approaches to AI that require searching through millions of neural network weights and at least dozens of architectural hyperparameters.Optimization problems consist of finding a maximum or minimum value of a objective function (in most practical cases, subject to a set of constraints).Depending on the inherent complexity and characteristics of the function to be optimized, there is a diverse armamentarium of ways to attack optimization problems.Methods including Linear and Nonlinear programming, Dynamic programming, Calculus of Variations, and Optimal Control have long been successfully used on smaller, traditional problems and can in some cases be completely dominant in a given area of application.