Reviewerly/exHarmony huggingface dataset:
Difficult on Addressing Security: A Security Requirement Framework

For any security updation, software must be secure that can be analyzed at the early stage of requirements. All security analysis is prejudiced approach which follows certain rules, defined-laws. Models and policies made the software worthwhile. However, in today scenario, still there is a deficiency in security requirements. It has been observed that to capture security requirement a business goals must be fulfilled that helps to protect assets from threats. This is the reality that any security violation is caused openly by vulnerable software. A scrupulous review has carried out regarding the fact as there are many approaches that consists of policy, rules or any guidelines for secure requirement phase. Therefore, it is desirable to develop a prescriptive framework that addresses security at requirement phase. The chronological approach of security requirement framework is presented that helps security experts to analyze security and mitigate threat at requirement phase.
Information Hiding in Text Steganography: A Different Approach

Today's internet world is flooded with huge amount of data which is resulting data leakage and theft of information. Lots of challenges arise when transferring or receiving various kind of covered data, messages or information especially using the public networks. Cyber crime one of the biggest imperfection in this fully connected internetworking world. Cyber attacks, phishing attacks, intruder like third party are always active to hack official, personal or governmental information in the form of a message using this networks. This is why we introduce the technique namely Steganography, where the tricky message has been hiding the data/information confidently. Steganography is one of the best techniques for putting out of scene for a message bounded by other media (cover) as if nothing is there. Here a number system is a cover media and text message hide over this cover media. In this paper a number of co-ordinates using by the innovative formula which used in an novel way to hide the actual message. The predestined receiver is always concerned with this algorithm. It can be much realized by the scholars as well as researchers to progress towards a pioneering area of research for secret message transfer.This work is not a evaluation or enhancement of the earlier works but it is a altered approach to text Steganography to hide the message. A fantastic combine of number, word to hide information. For developing the application this paper could be very useful for concealing secret data / information for Central Bureau of Investigation, Defence Intelligence Agency, Intelligence Bureau (IB) for sake of saving mother land.
Interaction Speed as Nonverbal Cues in Text Messaging via Smartphone

In text messaging with mobile phones and smartphones, many users face pressure to rapidly reply. Despite the increasing importance of mobile text-messaging, few studies have examined reply speed. This article focused on the speed of interaction in text messaging via smart phone applications (especially those with read receipt functions). It was suggested that the speed of exchange and read receipt status can serve as operable nonverbal cues to the sender of the message. Toward the aim of effective communication via text messaging using smartphones, we propose future directions in examining the strategic use of speed of interaction and read receipts.
Cognitive Map Query Language for Temporal Domains

This article introduces the temporal cognitive maps model and its associated query language. A cognitive map is a graph used to model strategies or influence systems. Each node represents a concept and each edge represents an influence. One limit of cognitive maps is that temporal features cannot be taken account in the model. This article proposes an extended model of cognitive map, called temporal cognitive maps, that includes temporal features in a cognitive map. This article also proposes a temporal cognitive map query language that accesses all the components of a temporal cognitive map: concept, influence and temporal features.
Analysis on Sales Using Customer Relationship Management

Customer relationship management (CRM) is an established concept which is used to manage customer and employee's lifecycle management through various techniques and process-oriented tools. The main reason of this CRM is to improve the relationship between customers and the company by using many modules like to examine of sales, client services, and many others. The objective behind this project is to increase the sales efficiency of the organization which returns more profit for the organization. In this paper, CRM includes few modules. Customer relationship management (CRM), it is troublesome not to see robotized chatbot changing these stages and empowering deals and advertising groups to all the more likely react to their clients. Some may scrutinize the humankind of CRM chatbot; however, there is no denying that opening up CRM information by utilizing creative devices helps speed the business cycle.
Real-time gesture control for automotive infotainment system

Drivers’ distraction is one of the leading causes for road accidents. To reduce driver distraction, the real-time gesture recognition system for ADAS is aimed to simplify and enhance the interaction between human and computer by implementing the vision-based technique that allows driver to interact with the vehicle infotainment system functions using natural mid-air hand gestures. Therefore, in this paper, we proposed to track and recognize human static hand gestures. The system process is separated into five steps which include the image acquisition, the background subtraction, the hand segmentation, the features extraction, and the gesture recognition. Firstly, the image frame will be captured and resized. Thereafter, the region of interest is determined to minimize the required processing to increase performance. Next, the foreground model is being extracted and being converted to HSV color space. Then, the skin filter is applied to extract skin region. In the next step, the image is being transformed into binary image by thresholding and smoothened by applying the morphological transformation. Contour detection and approximation were being implemented. Finally, the hand features will be extracted to build the gesture recognition model such as hand center, palm radius, fingertips, defect points, hull area, hand area and angle of finger. Experimental results show that the system is able to achieve 86.25% recognition accuracy in room environment and 80% recognition accuracy in car environment. In comparison, the average classification accuracy is 92.5% and 90%, for room and car environment, respectively.
User authentication based on smartphone application usage patterns through learning classifier systems

Smartphones have become more ubiquitous than ever. People are installing various applications on their smart-phone to fit into their lifestyle. Existing research shows that there are patterns within the ways people access those applications, whether it involves particular locations, particular ranges of time, or many other factors. In this research, through a collaboration with a commercial company, we collected usage data from a popular smartphone application that gives its users access to digital flyers information for shops and supermarkets throughout Japan. Our early experiments found that the pattern information contained inside the data could be used to authenticate users. In this research, we are proposing a behavioral authentication model implementing customized learning classifier systems to search through vast amount of possible patterns to authenticate users of the application. Our early findings for this ongoing research demonstrate that our model can feasibly be a good alternative for additional authentication factor to implicitly authenticate users beyond the initial registration.
Towards Securing Cloud &amp; Information - Vision &amp; Challenges

Even being at its nascent phase, Cloud Computing has demonstrated exceptional solutions for solving the modern world computations. Cloud Computing has changed the perceptions for the grid computing, utility computing, distributed computing and has overshadowed the traditional server client computing architecture. Accessibility of data and services over the internet has been made easier with the implementation of Cloud computing. However, still many security challenges persist that tend to weaken the vision for adopting Cloud architecture. With the evolution of the Cloud and its deliverables over the period of time, it is the urgency of the hour to equally address and derive the necessary resolutions for the security challenges. The paper is aimed to demonstrate a systematic view of the existing efforts for securing the cloud and their benefits and pitfalls. The objective is also to study and determine the driving forces which makes the Cloud more reliable, approachable and beneficial for adoption. Finally, we aim to provide future challenges and opportunities to present a wide scope of research in the same direction.
AML-SVM: Adaptive Multilevel Learning with Support Vector Machines

The support vector machines (SVM) is one of the most widely used and practical optimization based classification models in machine learning because of its interpretability and flexibility to produce high quality results. However, the big data imposes a certain difficulty to the most sophisticated but relatively slow versions of SVM, namely, the nonlinear SVM. The complexity of nonlinear SVM solvers and the number of elements in the kernel matrix quadratically increases with the number of samples in training data. Therefore, both runtime and memory requirements are negatively affected. Moreover, the parameter fitting has extra kernel parameters to tune, which exacerbate the runtime even further. This paper proposes an adaptive multilevel learning framework for the nonlinear SVM, which addresses these challenges, improves the classification quality across the refinement process, and leverages multi-threaded parallel processing for better performance. The integration of parameter fitting in the hierarchical learning framework and adaptive process to stop unnecessary computation significantly reduce the running time while increase the overall performance. The experimental results demonstrate reduced variance on prediction over validation and test data across levels in the hierarchy, and significant speedup compared to state-of-the-art nonlinear SVM libraries without a decrease in the classification quality. The code is accessible at https://github.com/esadr/amlsvm.
Cyberbullying Detection and Prevention: Data Mining in Social Media

This article is devoted to analysis of the articles on cyberbullying of children and adolescents and creating the methodology of working on the own work, which related especially to Kazakh social media. The article provides examples of similar works by foreign researchers, the United States, England and other European countries. Using the methods of theoretical, namely analysis, synthesis, and empirical: comparison and experiment, the analysis of works on this topic was carried out, and this work was aimed at analyzing the problem in the Kazakh space. In brief, we consider the issue of creating a parser, as well as collecting data for training machine learning and deep learning algorithms that could find and block texts containing a humiliating slope in real time. The article will be of interest to both novice specialists who are engaged in data analysis, and experienced ones to expand their horizons.The question of implementing deep learning algorithms requires further study, which is one of the parts of a large work being done, as well as a future topic for a subsequent article.
Directions of Development of Independent Assessment of the Quality of Educational Activities of Educational Organizations: Regional Experience

Purpose: This article reflects the results of the authors’study of education quality assessment in the Krasnoyarsk Territory. We consider the problem of transition to professional standards and attestation of educational workers that has been becoming more and more urgent lately. Design/Methodology/Approach: Our research aims to critically evaluate the activity on creating an effective national qualification system. Findings: The authors have come to the conclusion that the professional standard “Pedagogue (pedagogical activity in the field of preschool, primary general, basic, secondary general education) (educator, teacher)” is being quite actively introduced in Russia and tested in educational institutions, among other things, for certification of teachers, which is evidenced by empirical data. An analysis of the Krasnoyarsk Territory case allowed to reveal key tendencies in dynamics of evaluation of teachers’ qualifications in the educational aspect in one of the central Siberian regions. In particular, our study concerned the data of the Regional National State-owned Specialized Institution “Education Quality Assessment Center”, which is engaged in organizational, methodological and informational support of the procedures for the State final certification and certification of teaching staff. Originality/Value: The authors come to the conclusion that it is necessary to establish several separate assessment centers in the Krasnoyarsk Territory: on preschool education, primary general, basic, secondary general education. These centers could concentrate on the development of competitive assessment tools, which would affect the quality of assessment of teachers and allow experts to concentrate on the point competences in various areas of teaching.
Supervised Word Sense Disambiguation on Polysemy with Neural Network Models: A Case Study of <i>BUN</i> in Taiwan Hakka

While word sense disambiguation (WSD) has been extensively studied in natural language processing, such a task in low-resource languages still receives little attention. Findings based on a few dominant languages may lead to narrow applications. A language-specific WSD system is in need to implement in low-resource languages, for instance, in Taiwan Hakka. This study examines the performance of DNN and Bi-LSTM in WSD tasks on polysemous BUNin Taiwan Hakka. Both models are trained and tested on a small amount of hand-crafted labeled data. Two experiments are designed with four kinds of input features and two window spans to explore what information is needed for the models to achieve their best performance. The results show that to achieve the best performance, DNN and Bi-LSTM models prefer different kinds of input features and window spans.
A Machine Learning-Based Approach to Detect Credit Card Frauds

Nowadays, the amount of online transactions has increased exponentially due to the increasing usage of Internet, thereby causing steep increase in frauds. The purpose of this paper is to propose a technology led model that can help detect credit card fraud cases. The credit card dataset is highly imbalance in nature; therefore, we have applied balancing technique on the dataset. The proposed approach is implemented and tested using four classifiers: random forest, Gaussian Naïve Bayes, logistic regression and K-nearest neighbour. We have taken a standard credit card dataset of a European bank. The performance of these classifiers is measured using precision, recall, F1 score, ROC curve. Results have shown that random forest has performed best with F1 score of 0.92.
Research on Human Reliability Model of a Reservoir Dam

Abstract The safety of reservoirs has a great impact on the social and economic life of the country, and human factors in modern life occupy an increasingly important position. This paper uses the prediction technology of human error probability, establishes the reliability model of human factor cognition based on Bayesian network, quantifies the influencing factors of human factor, and actually uses the calculation in a certain reservoir dam.
Creating leadership minds for the digital VUCA world-sculpting functional perceptual architectures via cultural expertise

Few phenomena have incited as much passion as the unravelling of what 'effective leadership' means in constantly changing environments. It is suggested here that for leading successfully in the digital VUCA a fundamental transformation of individual minds is necessary, due to the novel bio-cultural paradoxes posed by it. Such 'VUCA resilient' minds are claimed to emerge from a specific perceptual architecture built on new, functional connections set up via the integration of cultural otherness into highly interlinked perception, cognition, affect and action systems. The relevant neurobiological underpinnings of this architecture biasing vision are outlined and an innovative perceptuo-cognitive experimental pilot (n = 34) is introduced to visualise the resultant changes in vision. Eyetracking (implicit) and questionnaire (explicit) data are analysed in tandem to improve the quality of data. Based on such promising initial tendencies it is suggested to add visual tools to leadership assessments.
Research of edge detection methods in the task of pattern recognition on images with low contrast

The article discusses edge detection methods separately and combinations of edge detection filters with antialiasing filters in the task of pattern recognition on images with low contrast. Sobel, Canny, Otsu and thresholding filters are considered as edge detection methods. Median and Gaussian filters are considered as smoothing filters. The performance of the filters is assessed using the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM).
Master’s Degrees in Russia: The Reality of the Bologna System Implementation

This paper identifies the master’s students’ attitudes to the effects of Bologna reform in Russia. The core idea is that the main challenges in Bologna Process implementation in Russia are connect deeply with the quality of human capital. Conclusions were based on the results of a large-scale survey, conducted in 2019, in 19 Russian universities. The authors examined the shortcomings of higher education reform at the micro-level. Data demonstrates that the reform provided a real opportunity for students to change their education track; work experience is more important for employers than master’s degree; master’s students enter the labour market as early as possible; master’s students are not eligible for entrepreneurship and collaborative group work skills; most master’s students believe that hard skills are crucial for any future job. The transition to a two-cycle system is assessed rather negatively, both by students and teachers. This paper also opens a discussion for more use for the Bologna reform implementation sustainable development framework.
Reasoning about distributed information with infinitely many agents

Spatial constraint systems (scs) are semantic structures for reasoning about spatial and epistemic information in concurrent systems.We develop the theory of scs to reason about the distributed information of potentially infinite groups.We characterize the notion of distributed information of a group of agents as the infimum of the set of join-preserving functions that represent the spaces of the agents in the group.We provide an alternative characterization of this notion as the greatest family of join-preserving functions that satisfy certain basic properties.For completely distributive lattices, we establish that the distributed information of c amongst a group is the greatest lower bound of all possible combinations of information in the spaces of the agents in the group that derive c.We show compositionality results for these characterizations and conditions under which information that can be obtained by an infinite group can also be obtained by a finite group.Finally, we provide an application on mathematical morphology where dilations, one of its fundamental operations, define an scs on a powerset lattice.We show that distributed information represents a particular dilation in such scs.
VST3D-Net:Video-Based Spatio-Temporal Network for 3D Shape Reconstruction from a Video

In this paper, we propose the Video-based Spatio-Temporal 3D Network (VST3D-Net), which is a novel learning approach of viewpoint-invariant 3D shape reconstruction from monocular video. In our VST3D-Net, a spatial feature extraction subnetwork is designed to encode the local and global spatial relationships of the object in the image. The extracted latent spatial features have implicitly embedded both shape and pose information. Although a single view can also be used to recover a 3D shape, more rich shape information of the dynamic object can be explored and leveraged from video frames. To generate the viewpoint-free 3D shape, we design a temporal correlation feature extractor. It handles the temporal consistency of the shape and pose of the moving object simultaneously. Therefore, both the canonical 3D shape and the corresponding pose at different frame are recovered by the network. We validate our approach on the ShapeNet-based video dataset and ApolloCar3D dataset. The experimental results show the proposed VST3D-Net can outperform the state-of-the-art approaches both in accuracy and efficiency.
Human Action Recognition using Pre-trained Convolutional Neural Networks

Recognition of human action is one of the challenges in the field of artificial intelligence. Deep learning model has become a research issue in action recognition applications due to its ability to outperform traditional machine learning approaches. The Convolutional Neural Network is one of the architectures commonly used in most action recognition works. There are different models in the Convolutional Neural Network, but no study has been done to evaluate which model has the best performance in understanding human actions. Thus, in this paper, we compare the performance of two separate pre-trained models of deep Convolutional Neural Network in classifying the human actions to identify the different behaviours. GoogleNet and AlexNet are the used two models with fine-tuned parameters used for comparison, in addition, to use Long-Short Term Memory for the video's labels prediction. The paper's main contribution is that it offers a performance analysis of two separate fine-tuned deep CNN pre-trained models compared to the results of other recently proposed human action recognition methods applied on KTH, Weizmann, UCF11(YouTube actions) and UCF-Sports datasets.