Reviewerly/exHarmony huggingface dataset:
A two-phase gradient based feature embedding approach

• OCR is imbibed with spatial domain steganography to make the latter more secure. • Character level features of text image are embedded instead of image itself. • Quantity of embedded message for each pixel is varied to ensure least distortion. • One of the two candidate feature sets is embedded which causes least distortion. • As features are of no use without trained OCR model, this makes the method safer. Over the years, researchers attempted to maximize the capacity, imperceptibility, and robustness of Steganography algorithms. Nowadays, the challenge is not only to maximize these parameters rather identify new ideas toward the security aspect. Keeping this requirement in mind, in the present scope of the work, we introduce a novel Steganographic approach mingled with optical character recognition (OCR) techniques to maximize the security against intruders who aim to extract the hidden data. We introduce two novel feature descriptors, the modified shadow and percentage of light source (PLS) feature specifically keeping in mind the embedding requirements. We train our model on a standard publicly available dataset, also reasoning the idea behind doing so. In our embedding method, we vary the quantity of the embedded message for each pixel, based on the gradient value of that pixel, to ensure the least distortion which human beings cannot differentiate. Here, the message image (text image) is not embedded in its entirety, rather character level features, extracted from the text message, are then embedded in the cover image. This very phase ensures that even if the intruder has the access to the stego image, s/he cannot extract the data as it is not having the raw data rather some features. Even the feature values alone would not give the notion of a secret image as the intruder requires the trained OCR model, which the receiver has, to recognize the text. Furthermore, for every character image extracted from the text, we choose between two candidate feature sets and embed the one which causes the least distortion. The feature extraction step generates high recognition accuracy, which in turn completes the extraction process. The security model goes on to show good results in terms of the Steganography metrics. We also perform a fusion of standard robustness and statistical tests, whereby our embedded image successfully performs well.
A Time-Variant QoE Model Based on Real Video Streaming Data

In a video streaming system, the user’s experience is affected not only by the average video quality but also by some disturbing events such as rebuffering, bitrate switch, etc. How to combine these factors and accurately model the overall user-perceived quality-of-experience (QoE) is a challenging problem. In this paper we propose a time-variant QoE model that takes into account the temporal properties of rebuffering events during the user’s watching process. From data collected in a real video streaming system, we observe that the user’s tolerance to rebuffering changes dynamically with the video playing progress. Based on that, a novel QoE function is defined, in which the penalty weight of each rebuffering event varies with its occurring time. Experimental results show that the proposed model fits better to subject-rated QoE databases, and also brings improvement to the performance of rate adaptation algorithms.
Patterns Count-Based Labels for Datasets

Counts of attribute-value combinations are central to the profiling of a data set, particularly in determining fitness for use and in eliminating bias and unfairness. While counts of individual attribute values may be stored in some data set profiles, there are too many combinations of attributes for it to be practical to store counts for each combination. In this paper, we develop the notion of storing a "label" of limited size that can be used to obtain good estimates for these counts. A label, in this paper, contains information regarding the count of selected attribute-value combinations (which we call "patterns") in the data. We define an estimation function, that uses this label to estimate the count of every pattern. We present the problem of finding the optimal label given a bound on its size and propose a heuristic algorithm for generating optimal labels. We experimentally show the accuracy of count estimates derived from the resulting labels and the efficiency of our algorithm.
Extraction of Cerebral Hemorrhage on CT Images Using Level Set Algorithm and Otsu Threshold

Extraction of cerebral hemorrhage on CT images has always been the focus of several research hotspots and is still challenging as it does not show clear boundary. In this paper, a novel segmentation framework is presented for extracting the cerebral hemorrhage in brain CT images with weak boundary. Firstly, we utilize the Otsu threshold algorithm to get the coarse outline approximate to the target boundary as the initial curve of level set algorithm. Then, the active contour model is employed using both edge information and global Gaussian distribution fitting energy of images to modify energy function of level set. The proposed approach is applied on real images which from Quzhou People’s Hospital. Compared to manual delineation, the proposed technique shows a higher JS value than the existing methods and requires less interaction which is listed in the literature.
Research on Crowd Counting Based on Attention Mechanism and Dilation Convolution

Aiming at the problem of low accuracy of population counting in existing algorithms, we propose a density estimation algorithm combining attention mechanism and dilated convolution. In this paper, the basic framework of feature extraction consists of part of the network layer of VGG-16 and attention mechanism. Then, replace part of the pooling layer and fully connected layer of the original network with a zigzag dilation convolution module to effectively compensate for the information loss caused by the pooling layer. Specially, the ability of the network model to extract features is improved by fusing the feature information of the high and low layers, thereby improving the counting performance of the model. We compare our method with the other state-of-the-art works, and the experiment results demonstrate the superiority of our method, which shows that the proposed method has high accuracy, strong adaptability and robustness, and can well adapt to the detection of people of different densities.
Research on Semantic Sentiment Analysis Based on BiLSTM

This paper proposes an emotion classification model based on attention mechanism and Bi-LSTM. Firstly, the texts were trained into word vectors by word2vec model. Secondly, Bi-LSTM network was used to learn the semantic information of context. Then attention mechanism was added to extract the relatively important part of the texts for emotion classification. Finally, the activation function was used to classify the texts. This paper made an experiment of emotion classification based on IMDB data sets. The experimental results show that the proposed model is better than the traditional machine learning method and recurrent neural network in accuracy, recall and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> value. This proves that the AT-BiLSTM model proposed in this paper can effectively improve the effect of emotion classification, and has certain practicability.
Image Denoising Algorithm Based on Generative Adversarial Network

Abstract Image denoising is an important research direction of image restoration. With the increasing requirements of image quality, image denoising has been widely concerned by scholars at home and abroad. This paper studies the problem of noise image denoising, using improved sparse representation algorithm and deep learning technology to denoise the noise image. In this paper, a de-noising model based on generative countermeasure network is proposed. The residual algorithm and dense connection are added to the network layer of learning image noise information features. At the same time, the discriminator is trained to judge whether the generated image is true or false, so as to avoid boundary blur while de-noising. Zero filling convolution is used to ensure the consistency of input and output characteristic dimensions of each layer. The output of network model is image noise information. Simulation results show that the algorithm has good denoising performance.
Intelligent Traceable Cargo System in High-mobility and Connectionless Environment

The Internet of Things (IoT) concept is considered as an added-value to various application domains. Supply chain and cargo tracking are a few to name. However, the security of the IoT transactions and integrated devices remains one of the key challenges to the IoT application's success. Recently, Blockchain technology and its consensus protocols were promoted as a solution to many information security problems. This paper discusses the advantages of integrating Blockchain technology into IoT platform to improve security since it provides a distributed transaction ledger that cannot be tampered with or controlled by a single entity. The objective of this paper is twofold, first providing an overview of existing literature on the topic of integrating Blockchain and IoT platforms in a connection-less environment; Second, presenting the proposed traceable cargo system based on Blockchain and IoT.
Energy Levels Based Graph Neural Networks for Heterophily

Abstract The representation power of graph neural networks (GNNs) under heterophily has drawn much attention recently, in which some connected nodes do not have common labels or similar features. Previous approaches typically incorporate high-order neighbourhoods’ information so as to reinforce aggregation operation. However, they do not directly face with heterophily in first-order neighbours. To deal with this problem, we propose the energy levels guided GNNs in which energy levels of classes are incorporated for better expressiveness. We prove that the label with higher energy level would imply higher energy in the spectrum of the normalized graph Laplacian. Experiments on common benchmark datasets show the effectiveness of the proposed approach.
Assessing the Supervised Machine Learning Algorithms for Human Activity Recognition Using Smartphone

Smart phones have come all the way up to become an essential part of our lives compared to any other device. People use it during their free time, some people work on their smart phones, for social media, photos, retrieving information, etc. As it can be utilized in many different fields, human body medical and fitness are also a very important field that smart phones can be used to simplify the process of medication and human body surveillance. Imagine a mobile application constantly analyzing the movement of the body through the help of sensors present in our smart phone in real time, and looking through these values, it can detect the current state of a human body like walking, sitting, laying, standing, etc., altogether human activities. In this paper, we aim to test and analyze various machine learning algorithms such a way that it recognizes the current state of the body and predicts the activity being performed with maximum accuracy. The trained models can further be deployed for real-time use in either mobile devices or on computer using a website as interface. Most of the smart phone contains accelerometer and gyroscope which are the sensors built-in with the smart phone hardware. We have taken the dataset from the UCI machine learning repository.
Shadow Detection Using DenseUNet

The objective of this paper is shadow detection. We propose a novel semantic segmentation approach based on DenseUNet. The UNet is an encoder–decoder architecture consisting of an encoder and a decoder. The encoder is used for downsampling the image and the decoder for upsampling the image to produce the required output. Our proposed architecture consists of the basic UNet model in which a Dense network connects the encoder and the decoder. Outputs from each of the four encoder downsampling units are connected to each of the four decoder upsampling units through the Dense network. The proposed architecture was trained and evaluated on the SBU shadow image dataset and the results assert the effectiveness of our method as compared to the state of the art.
Supporting Agile Transformation: A Proposal for a Scientifically Founded Agile Assessment Model : Doctoral Paper

Agile methodology has been a trending topic over the past two decades. Organizations are establishing agile transformation programs to reap its benefits, and cope with the everchanging business and technology landscape. Yet, making sense of the manifold domains contributing to the success of an agile transformation, and orchestrating them with a great synergy is not a simple task. To navigate in this complexity and establish roadmaps, organizations primarily need to understand where they stand. There are a multitude of Agile Assessment Models (AAM) that are designed with the intent of identifying the state of an agile transformation in an organization. Designing an AAM is a valuable step towards this need, however, such models tend to lack vital aspects. Although “What” is needed seems straightforward, in our research, we are trying to focus on “Why” lacking aspects are vital, and “How” to enable effective and efficient value creation through Agility.
Integrated operation platform in project for development of a gas cleaning devices for ventilation systems

To reflect the status of project assignments the use of a workflow visualization tool is performed. For international projects with participation of multiple research contingents the specialized Odoo program package has been used.Assignment board for the fulfillment of a joint project for development of a air cleaning devices for industry systems of ventilation has been shown as an example.Using an integrated project operation platform minimizes backlog approval time. Performance on the project by contingents in different parts of the world is without delay, which makes the implementation of such projects competitive in the international market of research services.
Design and Development of a Secure Password Vault using RSA Parallelization

Recent research shows that, at least 65% of people reuse and 13% of people use the same password to protect all their accounts and devices. Many users resort to storing all their passwords in a device in plain text or other insecure methods like keeping simple passwords or the same password for all accounts. This necessitates to develop a secure password storage system for increasing the number of accounts that the users and corporates need to handle. The objective of this research paper is to design and develop a secure password vault by using the parallel RSA algorithm. Three-tier architecture of the web application development and deployment is done by using the WAMP stack, where C++ programming and foreign function interface are also used. The serial implementation of RSA has been made for comparison. RSA is one of the most secure algorithms that is currently in use and it is uncrackable. Prime factorization has been implemented and optimized by GNFS to generate keys in various sizes of prime numbers. Four different implementations of RSA the serial, and parallel with and without Extended Euclidean Algorithm (EEA) are experimented in this design. A detailed analysis on their execution times and reduction in computation time are also compared. From the comparison, it is inferred that, parallel RSA with EEA provides a lesser execution time.
The strategy of forming the research competence of students – architects by means of mathematics

Formation of students' research competencies is an urgent and important task of universities, which fits into the implementation of the strategy of scientific and technological development of the Russian Federation. The paper discusses the impact of project-based learning technology of mathematics on the formation of research competencies of students-architects. The distinctive features of this work are: the use of design technology of teaching mathematics on the basis of an educational architectural project; identifying the criteria and indicators for the level of research competence of architecture students The paper presents the results of experiments confirming the effectiveness of the proposed technology. The perspectives of using project technology in architects' training are discussed.
Socio-ecological discourse in education for the sustainable development of the North regions

The article deals with a new position of socio-ecological discourse in educating a younger indigenous generation for the sustainable development of the North regions. The concept “socio-ecological competence” has been disclosed as a key personal quality and a specific behavioral model of a younger generation. This concept provides integrity, interaction, interpenetration of cognitive, pragmatic, emotional, problem-solving stimuli, providing successful gap overcommig between knowledge, skills, and abilities of ecological and social focus, their practical application in the society, which a younger indigenous generation follows. The material for studies is a model of developing socio-ecological competence in the younger indigenous generation in the North regions. The theoretico-methodlogical analysis of the model has been grounded on the leading ideas of the sustainable development of the regional communities. Essential principles of socio-ecological discourse in educating a younger indigenous generation for the sustainable development of the North regions, KhMAD – have been disclosed. A model has been developed and tested, as well as content modules, development levels of the socio-ecological competence have been determined, basing on the principle of the socio-ecological discource parameters fulfillment. It has been proved that social and ecological discource is a key direction for the sustainable development of every district.
The Impact of Total Employee Involvement and Management Leadership on the Healthcare Effectiveness inYemen

This research was developed for the purpose of finding out the impact of total employee involvement and management leadership on organizational effectiveness in the health sector in Yemen. The main problem of this research was found based on the research of Dureab et al. (2020) who indicated that the healthcare services in Yemen lack effectiveness, specifically over the COVID-19 crisis. The healthcare sector suffered from the high demand for its services, which almost become out of control.For this research, it was decided to use the quantitative approach. Because the quantitative approach was used, the data source will be primary data for this research. This research will use the questionnaire instrument to be the tool to collect the data. This research will be located at Sana’a city (the capital city in Yemen); because most of the healthcare institutions are located there. These questionnaires were distributed to the research samples, who were having the size of 379 employees in the healthcare institutions in Yemen.Regression test has been used to conduct statistical analysis from the primary data. Several tests have been conducted for the collected data. The main results found that there is a positive and significant relationship between total employee involvement and organizational effectiveness in Yemen. Also, there is a positive and significant relationship between management leadership and organizational effectiveness in Yemen.It has been observed that healthcare professionals in this group perceive the quality of health services they produce in their institutions as low. In order to eliminate this perception, it should be evaluated according to the leadership qualities of the managers selected under the responsibility of the top managers of the health institutions, and the workload should be reduced with the transfer of authority
Cloud Based Information System Architecture in Construction Site

Based on the research background of artificial intelligence technology, the application of artificial intelligence technology in intelligent building is studied. Firstly, the concept of artificial intelligence technology and intelligent building is analyzed. Secondly, on the basis of expounding the problems existing in the development of intelligent buildings, the paper explores the practical application of artificial intelligence technology in intelligent buildings, so as to provide reference for similar projects.
Development of Integrated Information Systems to Support Scientific Activity

The article is devoted to the development of technology fundamentals to create models, methods for information system development supporting scientific activities integrated into an open semantic space, consistent with international standards and development trends of both national and international information infrastructure.They performed the implementation of conceptual model and the developed linguistic support of the information support system in scientific and educational activities using the IRIS software platform of ICS SB RAS (Intellectual Distributed Information System of the Institute of Computational Technologies at SB RAS).The purpose of the system is to satisfy two types of information needs from researchers: the need for information about the sources of necessary scientific information and the need for the most necessary scientific information. The development of a conceptual model is based on DELOS DLRM and OAIS RM models. The main entities used in the model, as well as their classes and subclasses, metadata and relation types, are highlighted and discussed in detail. The model is based on the concept of a document as the main essence of the scientific information space, which includes such classes as competencies, training courses, organizations, data storage facilities, conferences, persons, publications, research projects, grants as well as relationships between them.As the part of distributed information system, access technologies to distributed information resources (collections, databases, etc.) development it is logical to provide access to thesauri within client-server technology using the single standard ANSI/NISO Z39.50.
How Context or Knowledge Can Benefit Healthcare Question Answering

Healthcare question answering (HQA) is a challenging task as questions are generally non-factoid. Recent neural systems are reported to have performance gains. However, little attention has been given to HQA as datasets are generally too small to train a neural model from scratch. Recently, several systems have been proposed to learn context representations for HQA. Despite moderate progress, these systems have not been thoroughly compared with state-of-the-art neural models, and the mentioned models are tested only on self-created datasets. To address the challenges, we develop a new joint model to incorporate both context and knowledge embeddings into neural ranking architectures. First, we adapt context embedding pre-trained from large open-domain corpus to small healthcare datasets. Second, we learn knowledge embedding from knowledge graphs to provide external information for understanding non-factoid questions. To evaluate our framework, we adapt many state-of-the-art methods for general QA to HQA, by injecting the context or knowledge information only, or both of them. Extensive experiments are conducted to compare our approach with those adapted methods and current HQA systems. The results show that our approach achieves the state-of-the-art performance on both HealthQA and NFCorpus datasets. The source codes are available at https://github.com/emmali808/HQADeepHelper.