Reviewerly/exHarmony huggingface dataset:
The application of kernel Fisher identification method based on face symmetry in face recognition

In view of the successful application of kernel method in linear discriminant analysis and the validity of face symmetry to face recognition, this paper puts kernel method into the linear discriminant analysis method which integrates the face symmetry. Then a kernel Fisher identification method based on face symmetry is proposed (KSFDA). Experiments on Yale face database show that KSFDA has better recognition effect than KPCA and KFDA.
3D modeling of faults and stability analysis of Dardvey mine in Sangan iron skarn complex, Iran

The system of water flow into the Sangan mineral pit, located in NE Iran, has a direct relationship with the deformations, fractures, and faults. This study aims to investigates, identifies, and analyze the tectonic structures in the Sangan Dardvey deposit, and to determine the regional structural model, deposit structural patterns, and finally provide the deposit’s 3D formation model for water entry. The Sangan mining region is located at the end of one of the large-scale branching terminals of the Dorouneh fault, this block is considered a part of a tectonic escape in terms of regional tectonics, and most of the groundwater in the region can be attributed to its activity. The fault density map has been prepared by investigating the faults existing in the deposit area and analyzing the spatial density of faults. The map shows that three faults zones in the Dardvey deposit area are in the E–W, NE–SW, and NW–SE strikes with a dip angle (70°–80°). These fault areas can create paths for the entrance of groundwater into the mineral deposit. Moreover, the structural studies have shown that in the Sangan Dardvey deposits, the fractures play a crucial role in the fault zones, to which the existing anomalies are directly related. The estimation of water entry in the Dardvey deposits has been mainly controlled by tectonic structures.
TagRec: Trust-Aware Generative Adversarial Network with Recurrent Neural Network for Recommender Systems

Recently recommender systems become more and more significant in the daily life. Although the recommender systems based on the generative adversarial network (GAN) are competent, the user trust information is seldom taken into consideration. In this paper, we propose a Trust-Aware Generative adversarial network with recurrent neural network for RECommender systems named TagRec, which makes use of the user trust information for top-N recommendation. In the framework, the discriminative model is a multi-layer perceptron to distinguish whether a sample is from the real data or fake data. The discriminator helps to guide the training of the generative model to make it fit the data distribution of the user trust information. The generative model is a recurrent neural network (RNN) with long short-term memory (LSTM) cells, aiming to confuse the discriminative model by generating samples as similar as possible to the real data. Through the adversarial training between the discriminative and generative models, the user trust information can be fully used to improve the recommendation performance. We conduct extensive experiments on real-word datasets to validate the effectiveness of the TagRec.
Using Mobile Technologies Among Undergraduate Nursing Students for Academic Purposes in Tertiary Education

Mobile devices are increasingly part of daily life, with the benefits of using the technology in nursing education widely recognized. This study explored the use of mobile devices among undergraduate nursing students for academic purposes in South Africa, using a quantitative survey. The majority of participants owned smartphones (87.6%), followed by laptops (76%) and tablets (47.1%). Mobile devices were used to perform academic tasks and communicate and collaborate with peers and teachers, as well as search and access electronic resources. Few of the first year nursing students owned laptops and tablets and used them less frequently than the students from other levels of the study. Equipping nursing students with mobile devices, such as laptops and tablets, particularly first year students, and ensuring that they have adequate skills to use them, is essential to training future nurses who are expected to work in a technology-mediated health environment.
Software Reusability Estimation Using Machine Learning Techniques—A Systematic Literature Review

Software engineering is an application of engineering which is more focused on original development, but reusability plays a very significant role in order to produce good quality, error free, and less maintainable software. Software reusability is an attribute of quality which helps in selecting beforehand acquired notions in new statuses. Software reusability not only advances productivity, but it also provides a good quality software and has also optimistic effect on maintainability. Software reusability is advantageous in the manner that it provides high reliability, low cost of maintenance, and reduction in development time. In this paper, we have discussed and analyzed various machine learning techniques used for estimation of software reusability. It is found that machine learning techniques are competitive in nature with other reusability estimation techniques and can be used for estimation of reusability. This study will help software developers and information industry to elucidate that how software reusability can assist them in selecting advanced quality of software.
A Realization Framework of International Image Prediction System for Film and TV Works

International image is the window of a country to the world. This paper introduces how we use technologies such as reptile technology, natural language processing, clustering, and data visualization to collect relevant information about film and television works and analyze the internationally spread image of China's film and television works. In addition, an emotion dictionary is established, the emotion index is calculated, and the positiveness of the image is evaluated. Finally, a prediction system was established, and the two functions of the prediction of communication image and recommendation of communication strategy are realized through preset algorithms. The system can be applied to the work of cultural transmission to help build a national image consistent with China's long-standing culture and prosperity.
IMPLEMENTATION OF THE TOPIC ON CHAT-BOT DEVELOPMENT INTO "OFFICE COMPUTER TECHNOLOGIES" UNIVERSITY COURSE

The advisability of modifying the content of “Office Computer Technologies” discipline is reflected in this article by introducing “Development of chatbots with the help of free online development frameworks” topic implementation. The choice of this educational topic is due to the fact that a chatbot is one of the most promising information and communication tools to improve the efficiency of companies. The use of chatbots is one of the most relevant modern ways of communicating with users of mobile devices, which allows companies to reduce marketing costs and the cost of promoting their products and services. The implementation of chatbots can significantly speed up the process of interaction with customers; knowledge and skills in this technology are currently a popular competence for IT professionals in the business environment. The result of studying this topic while studying “Office Computer Technologies” discipline by students is the development of a bot-consultant to work in the Telegram platform, which provides brief information and answers to the company's activities. The implementation of “Development of chatbots with the help of free online development frameworks” topic to the content of “Office Computer Technologies” discipline corresponds to the main focus of educational programs of “Software Engineering”, “Computer Science” and “Information systems and technologies” of the first (bachelor) level of education. The experience of working with students of Kherson State University to study “Development of chatbots with free online resources” topic and the results of the pedagogical experiment showed that it can be successfully implemented into “Office Computer Technology” training course for bachelors - 121 “Software Engineering”, 122 “Computer Science”, 126 “Information Systems and Technologies”, as well as students of related specialties in order to motivate them to solve professional problems and to form research competences.
RSA Encryption and Decryption System

For communication in the wireless networking, transmission of data over network sometimes is not safe. In such case security of network is one of the essential aspect in computer networking. Cryptography is antechnique of transforming an plaindata into encrypted one, and then retransform that encrypted data back to its plain (original) form. In this we authenticate the sender to whom you want to send that data file. There are two different techniques of cryptography, symmetric key cryptography (called public-key cryptography) algorithms and asymmetric key cryptography (called public-key cryptography) algorithms. There are also various algorithms for encrypted data using either public or private key or both. This paper describes RSA algorithm which first convert our data into other form and then encrypt it using RSA public key encryption at sender side &amp; at receiver side, first it authenticate the receiver then decrypt the data/ file and convert into original form.
The Reform of Chemistry Technology Course System Based on Simulation Technology

By analyzing the textbooks of various versions of chemical technology and the actual situation of our college, we have integrated the relevant courses and practical links of chemical technology and chemical design, and formed the teaching model of "practice-theory-practice-theory-practice-practice". The application of simulation technology to the course system has formed the characteristic chemistry process course system.
Joint Extreme Channels-Inspired Structure Extraction and Enhanced Heavy-Tailed Priors Heuristic Kernel Estimation for Motion Deblurring of Noisy and Blurry Images

Motion deblurring for noisy and blurry images is an arduous and fundamental problem in image processing community. The problem is ill-posed as many different pairs of latent image and blur kernel can render the same blurred image, and thus, the optimization of this problem is still unsolved. To tackle it, we present an effective motion deblurring method for noisy and blurry images based on prominent structure and a data-driven heavy-tailed prior of enhanced gradient. Specifically, first, we employ denoising as a preprocess to remove the input image noise, and then restore strong edges for accurate kernel estimation. The image extreme channels-based priors (dark channel prior and bright channel prior) as sparse complementary knowledge are exploited to extract prominent structure. High closeness of the extracted structure to the clear image structure can be obtained via tuning the parameters of extraction function. Next, the integration term of enhanced interim image gradient and clear image heavy-tailed prior is proposed and then embedded into the image restoration model, which favors sharp images over blurry ones. A large number of experiments on both synthetic and real-life images verify the superiority of the proposed method over state-of-the-art algorithms, both qualitatively and quantitatively.
Gaussian Source Coding using a Simple Switched Quantization Algorithm and Variable Length Codewords

This paper introduces an algorithm based on switched scalar quantization utilizing a novel μ-law quantization model (optimized in terms of minimal distortion) and variable length code ...
Prospects of electronic publishing in Nigeria

One of the biggest challenges facing publishing in Nigeria today is how to integrate or adopt "epublishing" (electronic publishing).Across the world, the coming of computer has completely revolutionized the printing process from manuscript development through printing, marketing and distribution.The "destructive innovation" of e-publishing has worsened the fortunes of many news and academic publishers and major corporations have gone under as a result.Yet, stakeholders differ remarkably in their readiness to embrace the innovation.While some believe e-publishing should be approached and adopted with caution, many others believe it should be rapidly adopted.This study sought to find out the perception of electronic publishing by Nigerian publishers and end-users on: how beneficial do stakeholders consider e-publishing; how ready are end-users for electronically published products; and to what extent have stakeholders in the publishing sector deployed epublishing.The design for the study was quantitative-cluster and stratified sampling was used to strategically select 39 publishers and 150 end-users.A focus group discussion was conducted among stakeholders to give perspectives to the findings.The study revealed that publishers were considered the main beneficiaries of e-publishing, while authors and marketers benefit the least.End-users of electronically publishers are however more prepared than publishers for e-products.The news publishing sector has deployed e-publishing more than other publishing sub-sectors in Nigeria.
SPECIAL ISSUE: A New Generation of Statisticians Tackles Data Privacy

“What Should Privacy Mean to you?” was the heading of the last special issue of CHANCE magazine focused on data privacy and confidentiality, published 16 years ago (Volume 17, Issue 3). In that iss...
Cascaded Cross-Modality Fusion Network for 3D Object Detection

We focus on exploring the LIDAR-RGB fusion-based 3D object detection in this paper. This task is still challenging in two aspects: (1) the difference of data formats and sensor positions contributes to the misalignment of reasoning between the semantic features of images and the geometric features of point clouds. (2) The optimization of traditional IoU is not equal to the regression loss of bounding boxes, resulting in biased back-propagation for non-overlapping cases. In this work, we propose a cascaded cross-modality fusion network (CCFNet), which includes a cascaded multi-scale fusion module (CMF) and a novel center 3D IoU loss to resolve these two issues. Our CMF module is developed to reinforce the discriminative representation of objects by reasoning the relation of corresponding LIDAR geometric capability and RGB semantic capability of the object from two modalities. Specifically, CMF is added in a cascaded way between the RGB and LIDAR streams, which selects salient points and transmits multi-scale point cloud features to each stage of RGB streams. Moreover, our center 3D IoU loss incorporates the distance between anchor centers to avoid the oversimple optimization for non-overlapping bounding boxes. Extensive experiments on the KITTI benchmark have demonstrated that our proposed approach performs better than the compared methods.
Dynamic Simulated Annealing with Adaptive Neighborhood Using Hidden Markov Model

The Simulated Annealing (SA) is a stochastic local search algorithm. Its efficiency involves the adaptation of the neighbourhood structure. In this paper, we integrate Hidden Markov Model (HMM) in SA to dynamically adapt the neighbourhood structure of the simulated annealing at each iteration. HMM has proven its ability to predict the optimal behavior of the neighbourhood function based on the search history. An experiments were performed on many benchmark functions and compared with others SA variants.
The Development Strategy Research of Higher Education Management from the Perspective of “Internet + ”

As mankind enters the age of network information, profound changes happened in the working environment and mechanisms faced by college student management. Big data has become an important factor and technical environment for colleges and universities to implement the fundamental task of “building up people by virtue”. How to adapt to the development of the big data era and innovate the education and management of college students has proposed a new era mission. The research on college education management under the trend of “Internet + ” higher education management worldwide, reflects the characteristics of the times, and has the dual meaning of theory and practice. Educational thoughts such as constructivism, humanism, holistic theory, and flattening theory have an impact on higher education. This article analyzes and discusses the problems in China's high education management, and studies the development strategies of higher education management under the “Internet + ” perspective to promote the education management development.
Encoder Decoder based Nepali News Headline Generation

In this paper, a method for Nepali News Headline Generation is presented.The proposed method uses GRUs, in an encoder-decoder fashion, taking the news content as input and generating a headline as the output.The news is converted into word tokens, which are vectorized using FastText, trained on a corpus of Nepali news articles and headlines collected from several web portals.The headline generation model is also trained on the same corpus.A sequence to sequence model, with an encoder and a decoder GRU is used as the generation model.The model was able to attain a BLEU score of 22.1 on the test set.
Comprehensive Intelligent Information Security Management System (CIISMS) for Supply Networks: The Actor-Network Perspective

Actor-network theory based concept of spatialities was applied to the comprehensive intelligent information security management system composed of the three blocks: technical, psychological and legal. The incoming information security threats and the relevant system responses generated through the interaction of the system blocks were considered as enacting the three Law’s spaces: the space of regions, the space of networks and the space of fluids. The system stability in the space of networks is considered a precondition for its successful performance in the space of regions, and its resilience in the space of fluids gained through the dynamic knowledge formation helps overcome the adverse effects of the fluidity. We further reflect on the intentionality/unintentionality of the information security threats and reactivity/proactivity of the relevant system responses. The ANT-based topological approach presented in this paper in combination with other relevant methods has a potential of developing into an efficient theoretical tool for future research in the fields of information systems and information security.
AViNet: Diving Deep into Audio-Visual Saliency Prediction.

We propose the \textbf{AViNet} architecture for audiovisual saliency prediction. AViNet is a fully convolutional encoder-decoder architecture. The encoder combines visual features learned for action recognition, with audio embeddings learned via an aural network designed to classify objects and scenes. The decoder infers a saliency map via trilinear interpolation and 3D convolutions, combining hierarchical features. The overall architecture is conceptually simple, causal, and runs in real-time (60 fps). AViNet outperforms the state-of-the-art on ten (seven audiovisual and three visual-only) datasets while surpassing human performance on the CC, SIM, and AUC metrics for the AVE dataset. Visual features maximally account for saliency on existing datasets with audio-only contributing to minor gains, except in specific contexts like social events. Our work, therefore, motivates the need to curate saliency datasets reflective of real-life, where both the visual and aural modalities complimentarily drive saliency. Our code and pre-trained models are available at this https URL
Psychological readiness for the risk of innovative activities of teachers

The article is devoted to the description of the risks of innovation, the characteristics of approaches and the principles of their analysis. Particular attention is paid to the definitions of risk as a term of sociology and psychology. The structure and content of the psychological readiness of the subjects of the process to the justified risk of innovation are determined.
Conversational Agents in Health Care

To the Editor The Viewpoint by Dr McGreevey and colleagues1 drew attention to the increasing use of conversational agents (CAs) in health care. However, they suggested a less mature field than we find in practice, and their call for CA-specific regulations risks creating confusion.
Modelling and Simulation of Scrum Team Strategies: A Multi-agent Approach

Scrum is a type of agile process that incrementally, iteratively and continuously deliver software based on sprint time box. It is composed by User Stories, product backlog, sprint backlog, scrum team and sprints. Scrum team take user stories from product backlog into sprint backlog to start each sprint and deliver products at the end of each sprint. Sprint retrospective and review occurs at the end of each sprint to evaluate the delivered products and team performance. Based on the Scrum guide, scrum is easy to be understood but hard to be measured. Especially, it is depended largely on the performance of team dynamics referring to team compositions and task allocations, as its optimization make big impact on each sprint result. This paper investigating how solo and pair programming can make impact on scrum team performance based on several designed innovative team working strategies for both solo and pair programming by using agent-based modelling. Such innovative team working strategies for solo and pair programming have not been designed or applied into scrum context for investigation and evaluation based on agent-based modelling. As Scrum is a very complex environment, so that different strategies can be compared through modelling and simulation under various scrum context, such as scrum has different team composition, different task distribution and some randomly events to occur. A simulation tool is also designed and developed to carry on scrum team modelling and simulation which has realized all the designed strategies to carry on experiments and evaluation. The tool can simulate all types of scrum context and team composition to test designed strategies under various what-if assumptions in agent-based modelling.
Fast Probabilistic Neuro-Fuzzy System for Pattern Classification Task

The probabilistic neuro-fuzzy system to solve the image classification-recognition task is proposed. The considered system is a “hybrid” of Specht’s probabilistic neural network and the neuro-fuzzy system of Takagi-Sugeno-Kang. It is designed to solve tasks in case of overlapping classes. Also, it is supposed that the initial data that are fed on the input of the system can be represented in numerical, rank, and nominal (binary) scales. The tuning of the network is implemented with the modified procedure of lazy learning based on the concept “neurons at data points”. Such a learning approach allows substantially reducing the consumption of time and does not require large amounts of training dataset. The proposed system is easy in computational implementation and characterised by a high classification speed, as well as allows processing information both in batch and online mode.