Reviewerly/exHarmony huggingface dataset:
Video Audience Analysis using Bayesian Networks and Face Demographics

In this paper, we propose an approach to study and to model audience attention to videos within digital signage scenarios. An experimental setup was conceived to simultaneously display videos of various categories and to capture videos from the audience and surrounding environment. Face analysis via a deep neural network is performed to estimate gender and age groups. In the proposed approach, a Bayesian Network is built to model possible relationships between the audience's age, gender and face size (which is indicative of the distance to the display) and the video content types. A publicly available video dataset of 152 videos was created for displaying purposes. An experimental evaluation indicated varying degrees of attention to different videos, depending on age and gender. The area under the ROC curve of the built Bayesian Network was 0.82. The proposed approach allows to better understand the possible relationships between audience demographics and video contents, which may, in turn, be useful for displaying the most appropriate content to a particular audience, help with the automatic insertion of ads (based on audience categories), among other applications.
A Case Study on Teaching a Software Estimation Course

This paper describes a case study in designing and implementing a graduate-level course on software project estimation. The course’s objective was to introduce modern project estimation theory and practice through the process of learning through experience and reflection. We describe the goals and purpose that guided the course’s design and summarize our experience from teaching it for the first time in the 2018 spring semester. Our case study shows that a graduate-level course on software estimation can introduce several size and effort estimation methods, and that there are some affordable tools that can be used in academia. At the end students rated well our course and all of them would recommend it to a friend.
Fast Instance Segmentation for Line Drawing Vectorization

In this paper, we present a fast raster-to-vector method for line art images based on a convolutional neural network (CNN). State-of-the-art approaches for vectorization are very slow because they mostly consist of multiple steps including iterative optimization during the inference. In contrast, our model is based on a simple CNN extending a proposal-based instance segmentation algorithm. Therefore, it is very fast and end-to-end trainable. We experimentally show that our model is about 100 times - 1000 times faster than the previous vectorization methods without sacrificing accuracy very much.
Abstract Model of Eavesdropper and Overview on Attacks in Quantum Cryptography Systems

In today's world, it's almost impossible to find a sphere of human life in which information technologies would not be used. On the one hand, it simplifies human life - virtually everyone carries a mini-computer in his pocket and it allows to perform many operations, that took a lot of time, in minutes. In addition, IT has simplified and promptly developed areas such as medicine, banking, document circulation, military, and many other infrastructures of the state. Nevertheless, even today, privacy remains a major problem in many information transactions. One of the most important directions for ensuring the information confidentiality in open communication networks has been and remains its protection by cryptographic methods. Although it is known that traditional cryptography methods give reasons to doubt in their reliability, quantum cryptography has proven itself as a more reliable information security technology. As far is it quite new direction there is no sufficiently complete classification of attacks on quantum cryptography methods, in view of this new extended classification of attacks on quantum protocols and quantum cryptosystems is proposed in this work. Classification takes into account the newest attacks (which use devices loopholes) on quantum key distribution equipment. These attacks have been named <; <; quantum hacking>>. Such classification may be useful for choosing commercially available quantum key distribution system. Also abstract model of eavesdropper in quantum systems was created and it allows to determine a set of various nature measures that need to be further implemented to provide reliable security with the help of specific quantum systems.
GRU-Based Estimation Method Without the Prior Knowledge of the Noise

The state estimation method is one of the important techniques for target tracking and those techniques based on Kalman filtering have attracted the attention of many researchers. Among these existing methods, the very basic and necessary premise is that the model of the target is with high precision and good prior knowledge of process noise as well as measurement noise are also available. However, it is quite difficult to achieve in the practical applications. Therefore, in this paper, the Gated Recurrent Unit (GRU)-based estimation method is proposed to estimate the actual moving trajectory via the simulated GPS data with noise. GRU has a good advantage in the processing of time series data, and it has a memory function for data information. In the experiment part, by comparing with the traditional Kalman method, it is found that the GRU can obtain better estimation results.
A Composite Framework to Promote Information Security Policy Compliance in Organizations

Information security policy (ISP) noncompliance continue to impede information security in organizations. This paper consolidates the strength of previous studies into an effective single solution. The paper, first, synthesizes the existing literature and groups relevant ISP compliance factors into user involvement, personality types, security awareness and training, behavioral factors, and information security culture. Secondly, a generic framework that guides the development of frameworks for ISP compliance in organizations was developed based on the literature review. The generic framework categorized elements required for developing an ISP compliance framework into structure, content and outcome elements. Thirdly, the generic framework was applied to develop a composite ISP compliance framework that proposes the establishment of ISP compliance as a culture in organizations. Finally, the results of the expert review assessment showed that the proposed composite ISP framework was suitable, structurally sound and fit for purpose.
Comparing the logic programming between Hopfield neural network and radial basis function neural network

Logic programming is a superior language because it operates on a higher level of mathematical or logical reasoning. Logic programming is well-suited in building the artificial intelligence systems. In this paper, we reviewed the performance of the logic programming in Hopfield Neural Network (HNN) and Radial Basis Function Neural Network (RBFNN). Logic programming by using the Embedding method will improve the performance of RBFNN. In HNN, the logic programming can be implemented by finding the optimal synaptic weight via Wan Abdullah method. RBFNN is expected to do logic programming optimally compared to HNN. This study gives an overview of HNN and RBFNN regarding architectures, learning processing, and their application in 2 Satisfiability (2SAT) logic programming. Both networks will be assessed based on accuracy, sensitivity, and robustness. Pursuing that, RBFNN is expected to outperform HNN in doing 2 Satisfiability logic programming.
MC-HDCNN: Computing the Stereo Matching Cost with a Hybrid Dilated Convolutional Neural Network

Designing a model to quickly obtain an accurate matching cost is a vital problem in the stereo matching method. We present an algorithm called MC-HDCNN, which is based on hybrid dilated convolution neural network, for computing matching cost of two image patches. HDCNN uses the dilated convolution of the series to obtain a larger receptive field, while avoiding the "gridding" effect and ensuring the integrity of the receptive field. In addition, by adding batch normalization layer after each layer of the convolution, the gradient dispersion in the backward propagation and the generalization of the network can be improved effectively. We evaluate our method on the KITTI stereo data set. The results show that the proposed algorithm has certain advantages in accuracy and speed.
Archival Information Systems: New Opportunities for Historians

This paper presents a brief summary of the twenty years of research carried out at the A. P. Ershov Institute of Informatics Systems SB RAS in the area of developing electronic archives for heterogeneous documents. The phenomenon of electronic archives emerged and has been developing as part of the Novosibirsk school of informatics, which has always been oriented towards the contracting of social services. In the 1970s, the first social service projects launched by the Institute were educational initiatives for school education, accordant with the well-known thesis of Andrey Ershov: “Programming is the second literacy”. Over the years, the IIS SB RAS has completed a range of projects on digitizing historical and cultural heritage of the Siberian Branch of the Russian Academy of Sciences: the Academician A. P. Ershov Electronic Archive, SB RAS Photo Archive and the SB RAS Open Archive. This work has become especially relevant in view of the ongoing restructuring of the Russian academic science.
A Forgetting Property of Reciprocal Chains

Reciprocal chains (RC) are a class of discrete-index, finite-state stochastic processes with a generalized Markov nearest-neighbour property. All Markov chains (MC) are RC but not conversely. It is well known that a sufficiently regular MC “forgets” its initial distribution in a geometric manner. This article addresses the issues of forgetting for RC. Using the properties of inhomogeneous products of nonnegative matrices, this article establishes that a RC forget its endpoint distributions in a more complicated manner. In particular, this article shows that the forgetting function for the initial conditions is bounded by a superposition of increasing and decreasing geometric terms. Implications for modeling using RC are then discussed. The particular case of those RC that are MC with prescribed marginal distributions on the endpoints is considered. The tightness of the forgetting bounds is illustrated with numerical examples.
Increasing web server performance using the web balancing method

Abstract The high level of activity of internet users, which will have a negative impact on information providers. Requests from many users can cause performance capabilities on the web-server to decrease. The large number of requests results in crashes or overloads that have an impact on requests that cannot be served by a single server. Web balancing technology is needed so that the incoming request load is not only served by one server. Some scheduling algorithms that will be applied to web balancing testing in this study are round-robin and weight round-robin. In this study using ipvsadm as a means of making web balancing. This test aims to determine the performance of web servers when using web balancing technology by measuring the QoS parameters of web server services. The results show, the best value for measuring QoS parameters is respons time, request error, throughput, in the weight round-robin scheduling algorithm method.
Design of Internet-based Enterprise Accident Information Platform

Using Internet HAT information collection process to collect data, through Internet technologies suchas mobile law enforcement equipment and automatic identification software for major hazards to promote the formation of unified and standardized information security inspection, using the functions of synchronous interactive sharing and collaborative office between APP data and PC management data of mobile intelligent terminal, the accident information data is stored and managed in the Internet cloud, and an accident information sharing platform based on mobile intelligent terminal_government client_enterprise clien_related service agency client is built. It realizes the three-dimensional prevention and control network of fixed-point monitoring, mobile law enforcement, enterprise self-check and self-report production safety management, and provides strong technical suppor and service for Science and technology maintaining security.
Case-Based Genetic Optimization of Web User Interfaces

The combination of case-based approach and genetic optimization can provide significant boost to effectiveness of computer-aided design of web user interfaces (WUIs). However, their integration in web design domain requires certain sophistication, since parts of available solutions cannot be reused directly, due to technical and legal obstacles. This article describes evolutionary algorithm for automatic generation of website designs, which treats parameters of functionality, layout and visual appearance as the variables. The structure of the chromosome is devised, allowing representation of websites’ properties in the above three manipulated aspects and facilitating easy application of the genetic operators. We also describe organization and population of repository of filler-up content, which is compulsory for evaluation of WUI fitness with regard to the needs and preferences of users. We demonstrate retrieval of web designs as cases and propose using similarity measure in the fitness function to adapt the generated WUI to these examples. Finally, implementation of the approach is illustrated based on the popular Drupal web framework. The results of the study can empower case-based reuse of existing web designs and therefore be of interest to both AI researchers and software engineers.
Convolutional Networks Used to Classify Video and Audio Data

Abstract Deep learning is a kind of machine learning, and machine learning is a kind of artificial intelligence. Machine learning depicts groups of various technologies, and deep learning is one of them. The use of deep learning is an integral part of the current data classification practice in today’s world. This paper introduces the possibilities of classification using convolutional networks. Experiments focused on audio and video data show different approaches to data classification. Most experiments use the well-known pre-trained AlexNet network with various pre-processing types of input data. However, there are also comparisons of other neural network architectures, and we also show the results of training on small and larger datasets. The paper comprises description of eight different kinds of experiments. Several training sessions were conducted in each experiment with different aspects that were monitored. The focus was put on the effect of batch size on the accuracy of deep learning, including many other parameters that affect deep learning [1].
Why it's time for MSPs to sharpen their security focus

When huge businesses, government offices and even entire towns fall victim to data breaches, hacks and ransomware, it tends to grab the headlines. What these headlines miss is that smaller businesses are far more vulnerable to these attacks. Last year, a Ponemon Institute study on the state of cyber security found that 70% of small and medium-size businesses (SMBs) experience cyber attack attempts. 1, 2 And despite SMBs facing the same level of threat as big enterprises, over 50% don't know how to protect their businesses. Small and medium-size businesses (SMBs) are susceptible to cyber attacks because they don't have in-house expertise and they lack the deep pockets that could buy the tools and skills to protect themselves. While SMBs may feel helpless in the face of ever-more-sophisticated cyberthreats, the good news is that these companies are not alone. Just as they rely on their managed service provider (MSP) partners for their IT needs, they can also turn to MSPs to make sure their IT estates are secure. All MSPs have insight and knowledge on security, argues Tim Brown of SolarWinds MSP.
TRANSFORMATIONAL LEADERSHIP AS AN ALTERNATIVE FOR THE MANAGEMENT OF EDUCATIONAL CENTERS

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 1312-1318ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0397Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
A Spanish E-dictionary of Collocations

We present a new e-dictionary of Spanish (in progress) called Diretes (DIccionario RETicular de ESpañol).It contains descriptions of collocations by means of Lexical Functions (LFs), both standard and non-standard, in the sense of the Meaning -Text Theory by Igor Mel'čuk.At present, Diretes contains about 50,000 collocations.This paper concentrates on the collocations in which the collocate is an adjectival or an adverbial phrase.These collocations are mostly extracted from the Práctico combinatorial dictionary of modern Spanish.We explain the structure of the e-dictionary, the types of information it contains and the way it is presented.We also show how the LF-interpreted collocations can be used in NLP applications.We demonstrate it with the SemETAP semantic analyzer, in which LFs are used to normalize semantic structures and make inferences.
In search of the shortest possible schedule

The Communications Web site, http://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications , we'll publish selected posts or excerpts. twitter Follow us on Twitter at http://twitter.com/blogCACM http://cacm.acm.org/blogs/blog-cacm Bertrand Meyer considers how to speed up software engineering.
Dynamic Handwriting Analysis of the Character ‘Y’ for Writer Profiling Using Geometric Principles and Ratios

Graphology is the science of identifying a person or their emotional state based on their handwriting. Graphology is traditionally performed manually, the model proposed by this paper attempts to automate this process. The model proposed applies static handwriting algorithms to a dynamic handwriting system. The character written by the user is captured dynamically using a stylus. The static pipeline will focus on extracting geometric features of a written character and perform classification based on the ratios of the extracted features. This pipeline is then compared against the dynamic pipeline which uses the dynamic handwriting algorithm of dynamic time warping. The metrics obtained from testing the proposed system proves that the use of static algorithms on dynamic systems is still an accurate and valid classification method as the overall accuracy is significantly higher than the dynamic pipeline’s accuracy. The F1 scores for each class, within the static pipeline, further demonstrates that they were higher than the F1 scores from the dynamic pipeline. Furthermore, it was determined that the dynamic algorithm had the lower F1 score accuracy, however, this may be attributed to the small data sample used. It was found that the static features contributed more to the system than the dynamic features extracted.
ATIMO – A Tool for Alocating Agile Teams

It is essential for the success of a project to put together teams that meet the project requirements with lower cost and higher quality. Given this context, the present study developed a tool called ATIMO that uses the optimization algorithms NSGAII, SPEA2, and MOCell, to put agile teams together. The algorithms implemented in ATIMO were tested by being applied to four real projects in an experiment performed by a software development company. This approach took into account the project features, the developers’ profile, and both the project and the organization constraints. As a result, the algorithms returned solutions with the number of resources needed to carry out the project as well as the best qualified resources for the project with productivity and lower cost to meet the established deadline. The algorithms NSGAII, and SPEA2 presented similar results and behavior, as the MOCell algorithm presented a better performance in computational effort and required a larger population for its saturation.
An Improved Differentially Private DBScan Clustering Algorithm for Vehicular Crowdsensing

In response to the risks of privacy leakage caused by uploading vehicle participants' sensing data (such as location data or task sensing data) in the vehicle crowdsensing environment, an improved DBSCAN Clustering Algorithm (I-DP-DBScan) is proposed in the paper. The proposed algorithm is improved by optimizing the parameters Eps and MinPts, and differential privacy is adopted for guaranteeing participants' privacy. Laplace noise is added to the Euclidean distance of sensing data. Experimental results show that the I-DP-DBScan algorithm not only protects data privacy but also has good clustering efficiency.
Domain and Schema Independent Ontology Verbalizing

The semantic web has gained vast popularity among current computer scientists and engineers, due to its remarkable attribute of machine readability and human readability. As evidence of witnessing that popularity, thousands of semantic web based knowledge models have been designed, to solve various problems emerging from different domains that are almost freely available in numerous repositories in the web. Semantic web based knowledge models also referred to as ontologies, are domain rich conceptualizations. Hence, they have been widely used to gain computational intelligence, related to areas such as medical sciences, law, management and many other disciplines. Nevertheless, the complexity associated with semantic web-based knowledge representations such as RDF and OWL act as a serious bottleneck in reusability and knowledge dissemination linked with the semantic web. Knowledge retrieval from an existing ontology solely depends on schematics understanding and writing of appropriate SPARQL or SQWRL queries. This would become a really challenging hurdle for non-computer specialists such as medical consultants, lawyers, criminologists who also wish to experience the benefits of semantic web-based technologies. This research emphasizes on proposing a generalized verbalizer which could act as domain and schema independent ontology verbalizer for both RDF and OWL ontologies in human readable English. It is expected this generic verbalizer will widen the horizons of the semantic web-based technologies by opening new ventures for non-technical audiences.
Non-linear filtering of a phase tracking problem: Revisited

This paper revisits the phase tracking dynamics coupled with complex discrete non-linear observation. Non-linear filtering theory for the phase tracking problem with discrete observation equation is developed. Two continuous-discrete non-linear filters (i) the proposed higher-order filter and (ii) Extended Kalman Filter (EKF) are designed and applied to a phase tracking problem. Numerical simulations are carried out to test the effectiveness of the proposed higher-order filter in contrast to EKF. Simulation results of the paper reveal the usefulness of higher-order filtering for the phase tracking problem.
Designing Red Eye Reduction Software with Intensity Color Checking Technique

Abstract Photographs either with a regular camera or a digital camera where taken from high lighting on people, often producing red spots on the pupils of the eye with the term red eye. This can cause the photo results are not good. With certain software that functions as an image processor, it can easily remove the red eye using only a tool called red eye reduction. So with the existence of the program the results of photos with digital cameras can be edited to eliminate the red eye effect before printing. Red eye reduction used the intensity color checking algorithm in the process of replacing red image pixels and then replacing them with grayish black color according to the process of intensity produced. In this paper program implemented can reduce red eye with the intensity color checking algorithm by processing certain selected regions. The image results can then be printed or saved again in a bitmap format.