Reviewerly/exHarmony huggingface dataset:
Blockchain Marketplace—A Novel Overview for Real-Time Implementation

The renowned security concept called Blockchain is used everywhere nowadays. The Blockchain technology is introduced to the world after the development of bitcoin technology by Satoshi Nakamoto. Recently, many software companies and online marketing companies are trying to adopt this security technology. Except these sectors, other domains are also interested in Blockchain like machine learning, IoT, RFID, etc. In this paper, advancement of Blockchain technology is discussed in the online market sector. The workflow and system architecture of application is explained as well as the results of experimentation are visualized. The procedure to create autonomous Ethereum environment and ether is also explained.
On the Fate of the Temporal Existence of Man in the Technogenic Epoch: between Moments and Minutes

Introduction . In the technogenic epoch, people’s understanding of the nature of time has changed. The image of the high-tech time has formed and widely spread under the influence of modern technologies. Today, it has determined the nature of the temporal existence of person and has become, through the electronic clock, an instrument of its dehumanization. Anticipating these threats, the representatives of existentialism undertook the development of temporal problems starting from the middle of the XIX century. The existential analysis of the category “instant” as a specific dimension of the temporal existence of a person has taken a central place in it. Identifying the differences in the meaning of the terms “instant” and “minute” in philosophical and natural science interpretations, the author has suggested the possibility of humanizing modern technological processes by changing modern person’s concept of time. Methodology and sources. Hermeneutical and retrospective methods, methods of existential and comparative analyzes, the use of existential, cultural and interdisciplinary approaches have become the methodological basis of the work. The analysis of philosophical texts on the problem of changing the perception of time by person in the technogenic epoch (L. Mumford, M. McLuhan, D. Naisbitt), the works of existential philosophers devoted to temporal problems (S. Kierkegaard, N. Berdyaev, M. Heidegger) theoretical works on physics (S. Hawking, E. Sudarshan, J. Vaccaro), and a number of works on the humanitarian problems of the technogenic epoch was made (E. Balas, V. Tikhonova, V. Stepin, L. Kuznetsova). Results and discussion. The problem of overcoming the dehumanization of the temporality of human existence under the influence of high-tech time was considered in the article. The following conclusions were the results of the study of this problem. First, the reasons for the dehumanization of the temporal existence of a person acting in accordance with high-tech time have been identified. Secondly, the need to distinguish between the concepts of “minutes” of objective time and “moments” of the temporality of human existence was justified. Third, the study of temporal problems was defined as a condition for the humanization of scientific and technological development of modern epoch. Conclusion. In the technogenic, person’s free choice between minutes of high-tech time and moments of existential time have determined the fate of his / her temporal existence. Making one’s choice, a person has determined the character of the development of the technogenic epoch in the direction of the dehumanization of human existence or in the direction of the humanization of the technical world. The breadth of the study of the nature of human temporality and temporal issues in general will have affected the right choice.
MPNet: A Multiprocess Convolutional Neural Network for Animal Classification

Recent deep learning-based approaches have achieved remarkable performance in the animal-image classification field. However, previous deep learning-based approaches consume large amounts of computational resources, thus is not suitable for deployment under resource-constrained environments. To address this problem, we propose a novel Multiprocess Convolutional Network (MPNet). Specifically, this network contains two subnetworks. The first one employs a convolutional network to extract abstract semantic features from a horizontal viewpoint. To make full use of semantic information, we design the other subnetwork to extract features from a vertical viewpoint. Then we calculate the gram matrix of these feature maps by element-wise multiplication. Meanwhile, we adopt weight sharing strategy to reduce model parameters. Experiments on the Animals with Attributes (AWA) dataset has demonstrated that our proposed approach achieves 87.54% top-1 accuracy with 33.57MB parameters. Compared with the other state-of-the-art approaches, our model saves more computation cost and yields higher accuracy.
Role of Mobile Learning in Higher Education During COVID -19

In 21st century , the world is undergoing transformations due to rapid development of Information and Communication Technology (ICT) . By considering todays situation in covid-19, digital teaching and learning is a need in higher education. The corona virus pandemic and the social distancing made us to close to most of the universities worldwide and can help the students . In the period of COVID-19, when class room teaching is not possible, Mobile-learning (M-learning) provide students with digital literacy focusing on information processing. M-learning is accomplished with the use of small, portable computing devices like smart phones, palmtops, PDAs, cell phones, tablet PCs, laptops and personal media player. M- Learning facilitates a new mechanism of teaching and learning process in order to enhance the learning and education experience of the students. The use of mobile devices and wireless technology provides the new learning environments, allows students to achieve success in their educational process. M-learning encourage both teachers and students to take personal responsibility for their own learning. M-learning helps to bring a substantial change in the method of spreading knowledge to improve the quality in teacher education and hence will make teachers of global standard. Progression in mobile communication technology helps educational environments into interactive community. The paper addresses concept of m-learning, types of m-learning characteristics of m-learning and requirements for successful integration of m-learning in higher education. The circumstances of confinement caused by the corona virus, and to ensure continuity of teaching for students, mobile learning is an essential educational technology component in higher education.
Dual-cell differentiable architecture search for language modeling

Initial results of neural architecture search (NAS) in natural language processing (NLP) have been achieved, but the search space of most NAS methods is based on the simplest recurrent cell and thus does not consider the modeling of long sequences. The remote information tends to disappear gradually when the input sequence is long, resulting in poor model performance. In this paper, we present an approach based on dual cells to search for a better-performing network architecture. We construct a search space that is more compatible with language modeling tasks by adding an information storage cell inside the search cell, so that we can make better use of the remote information of the sequence and improve the performance of the model. The language model searched by our method achieves better results than those of the baseline method on the Penn Treebank data set and WikiText-2 data set.
The Research of Highway Construction Project Information Comprehensive Management System

Abstract With the development strategy of “Internet + Transportation’' put forward, the paper advances the process of the new type of transportation construction in the province. Compared with the common highway, the freeway design speed is higher, vehicle capacity is larger, so the quality is more strict during the construction process. This system helps highway construction managers to understand the construction of the project at the first time, and effectively deal with emergencies. Based on the actual situation of the project, this paper expounds the information management system and its functions of expressway construction project.
JUMPING FROG METHOD FOR OPTIMAL CLASSIFICATIONS

In the article the problem of finding optimal classifications on a finite set is investigated. It is shown that the problem of finding an optimal classification is generated by a tolerance relation on a finite set. It is also reduced to an optimization problem on a set of permutations. It is proposed a modification of the mixed jumping frogs to find suboptimal solutions of the problem of classification.
Coupling Adversarial Graph Embedding for transductive zero-shot action recognition

Zero-shot action recognition (ZSAR) aims to recognize novel actions that have not been seen in the training stage. However, ZSAR always suffers from serious domain shift problem, which causes poor performance. This is because: 1) Videos contain complicated intrinsic structures, including cross-sample visual correlations and cross-category semantic relationships, which make it challenging to generalize domain shift over categories and transfer knowledge across videos. 2) Existing methods do not disentangle unique and shared information underlying unseen videos during embedding. They are always weakly adaptive to novel categories and easily shift unseen videos to irrelevant action prototypes. In this paper, we propose a novel Coupling Adversarial Graph Embedding (CAGE) method for ZSAR, which formulates an effective visual-to-semantic embedding to alleviate the domain shift problem. Our model implements in a transductive setting that assumes accessing to a full set of unseen videos. Firstly, a structured graph is built for expressing both seen and unseen videos, which integrally captures visual and semantic relationships between them. Then, an effective visual-to-semantic embedding is formulated based on graph convolutional network (GCN), which is generalized to disjoint action categories and optimized for label propagation. In addition, a couple of adversarial constraints are proposed to characterize unique information of unseen videos and purify shared information across categories, which further improve the adaptability and discriminability of our model. Experiments on Olympic sports, HMDB51 and UCF101 datasets show that our model achieves impressive performance on ZSAR task.
A Language-based Approach to Analysing Flow Security Properties in Virtualised Computing Systems

This paper studies the problem of reasoning about flow security properties in virtualised computing networks with mobility from perspective of formal language. We propose a distributed process algebra CSP4v with security labelled processes for the purpose of formal modelling of virtualised computing systems. Specifically, information leakage can come from observations on process executions, communications and from cache side channels in the virtualised environment. We describe a cache flow policy to identify such flows. A type system of the language is presented to enforce the flow policy.
Priority-Based Optimized Scheduling (PBOS) Algorithm for Big Data Applications in Cloud Computing Environments

This paper proposes a priority-based optimized scheduling (PBOS) algorithm for cloud-based big data applications. In this algorithm, for each incoming task request, the task size and expected completion time are estimated. At the same time, for each VM on a host, the processing capability and current load are estimated. A priority queue for each task is then created in which the tasks are stored in the ascending order of task size and completion time. Similarly, a priority queue for VMs is also made in which the VMs are stored in the ascending order of current load and processing capacity. Then, the scheduling algorithm intelligently maps each user tasks from task queue to the suitable matching VM from VM queue. The process is continued until both these queue become empty. By experimental results, it is shown that PBOS achieves lesser response time and execution time.
Graph Discovery for Visual Test Generation

We consider the problem of uncovering an unknown attributed graph, where both its edges and vertices are hidden from view, through a sequence of binary questions about it. In order to select questions efficiently, we define a probability distribution over graphs, with randomness not just over edges, but over vertices as well. We then sequentially select questions so as to: (1) minimize the expected entropy of the random graph, given the answers to the previous questions in the sequence; and (2) instantiate the vertices that compose the graph. We propose some basic question spaces, from which to select questions, that vary in their capacity. We apply this framework to the problem of test generation in Visual Question Answering (VQA), where semantic questions are used to evaluate vision systems over rich image representations. To do this, we use a restricted question vocabulary, resulting in image representations that take the form of scene graphs; by defining a distribution over them, a consistent set of probabilities is associated with the questions, and used in their selection.
Hate Tweet Extraction from Social Media Text Using Autoencoder Wrapped Multinomial Naive Bayes Classifier

Social media is one of the biggest source for gathering real-time data, and information spreading in day-to-day life. In the name of freedom of speech, many people are using derogatory words and offensive languages which create a lot of problem for the concern person, organization, society, and government. To reduce the dehumanizing activities arises due to these hate speeches, we need to filter them from the social media text. Here, we propose a system which analyzes the micro-blog text to classify them as hate or non-hate speech and filter out the hate speeches. Our presented prototype system also handles non-English text as the social media platforms are not confined to English only. Various classifiers along with several learning techniques are used to exhibit their effectiveness for an accomplished solution. An autoencoder wrapped Multinomial Naive Bayes (MNB) classifier is incorporated in the system for a significant gain in the performance. The simplicity in its implementation makes our system superior to other state-of-the-art systems available in the literature. Finally, the prototype system is evaluated on various publicly available hate-related datasets to showcase its effectiveness on this challenging task.
E-learning in a pandemic era

The COVID-19 pandemic contributed to a digital economy transition by emphasizing the importance of digital infrastructure while exposing the digital gaps between countries and communities. Inequalities in digital access has impeded educational gains made towards achieving the sustainable development goals. While major studies have been carried out on digital learning there is currently a dearth of knowledge on the digital gaps and needs of students in remote communities in Nigeria. To this end, this study explored the digital gaps and needs of rural secondary schools in remote communities and its implications on e-learning across 6 Nigerian states during the COVID-19 era. The study adopted a concurrent embedded mixed method design approach to collect data from rural secondary schools from 6 Nigerian states. Findings from the study identified lack of ICT strategies and policies in Nigeria, socio-economic status, poor internet connectivity, electricity, and a high poverty level as the major drivers of digital gaps in remote communities.
Agency and Authenticity

Within the passages of a laudable attempt to provide a framework via which some clarity can be brought to questions regarding the ways in which differing aspects or “dimensions” of agency are inter...
Multi UAV Target Tracking Based on the Vision and Communication Information

This paper investigates the multi Unmanned Aerial Vehicle (UAV) target tracking mission based on the vision and communication information. Firstly, the multiple UAV platform is established to achieve the target tracking mission. Furthermore, utilizing deep learning algorithm, target detection is accomplished based on UAV vision. Thirdly, each UAV could communicate with others to share target information and track it by maintaining the certain distance. Finally, the effectiveness of multi UAV target tracking system is verified on the actual test.
Research on image steganography information detection based on support vector machine

With the rapid development of the internet of things and cloud computing, users can instantly transmit a large amount of data to various fields, with the development of communication technology providing convenience for people's life, information security is becoming more and more important. Therefore, it is of great significance to study the technology of image hiding information detection. This paper mainly uses the support vector machine learning algorithm to detect the hidden information of the image, based on a standard image library, randomly selecting images for embedding secret information. According to the bit-plane correlation and the gradient energy change of a single bit-plane after encryption of an image LSB matching algorithm, gradient energy change is selected as characteristic change, and the gradient energy change is innovatively applied to a support vector machine classifier algorithm, and has very good detection effect and good stability on the dense image with the embedding rate of more than 40 percent.
Shadow Detection with Attention Feature Block and Multi-scale Weight Segmentation Network

Shadow detection is an important and challenging task in computer vision. However, the current shadow detection method has limited semantic information extraction and has low detection accuracy for small shadow regions. Therefore, we propose a new shadow detection network that extracts both global semantic information and the context of the middle layer in a targeted manner. We use the single network structure of the encoder-decoder. The encoder module uses resnet-101 to extract features. The decoder module extracts semantic information by using the feature block with attention, which fuses the attention mechanism and divides the feature map, and uses the feature block after up-sampling to increase the resolution for feature extraction, so as to make it pay attention to the features of small shadow regions and improve the detection of small shadow regions. Furthermore, weight generator module and weight fusion module are used to combine the generated weight maps with the initial output of the network to further extract global context information. Finally, we use the pointrend module to complete the boundary refinement. We evaluate our network on the two benchmark datasets of SBU and UCF. The experimental results show that our method is superior to the state-of-the-art methods and the balance error rates on the two datasets are reduced by 20.0% and 10.7%, respectively.
Design of Sustainability of Solar Panel Integration in A Green Building Complex of Wonogiri Regent Office

Abstract With the opportunity to repair and rebuild office buildings in the Wonogiri Office Area, where previously area offices still scattered in various regions, now they will be moved and arranged in 1 location with the concept of City Forest—thus opening up opportunities for more environmentally friendly energy use (Renewable Energy). The implementation of Solar Panel rooftop for office buildings in the planning does not only consider the amount of electricity obtained from the photovoltaic installed for offices. However, it also considers the Sustainable Benefits factor to be more reliable and maintain its sustainability. Sustainable benefits in the Wonogiri Office can see from various factors such as; Social, Organizational, Technical, Economic, Environmental. The sustainability factor is assessed after considering the economic feasibility aspects (NPV, IRR, BCR, PBP, LCOE, NPC) and according to the Solar Power Plant installation’s technical aspects. This paper suggested help solves problems related to renewable energy projects that did not last long. It can also become a pilot project for other agencies in terms of a more sustainable Photovoltaic design. This paper will reveal emission reduction, components that need attention, and the willingness and ability to prepare for renewable energy.
In Memoriam: Donald “Digger” Herbert Gorman (1922–2020)

On 20 April 2020 the professional, industrial, and nonprofessional mineral communities alike lost one of the giants of this era. Donald Herbert Gorman, “Digger” as he was affectionately called by a...
Building a Bridge from Qualitative Analysis to a Simulation of the Arab Spring Protests

This paper builds a ‘bridge’ between a qualitative analysis and the design of an agent-based simulation by applying the CSNE framework, which distinguishes between context, scope and narrative elements. Qualitative data were constructed from ethnographic interviews on the Arab Spring in Egypt and Morocco. To identify narrative elements, the data were analysed by coding procedures from grounded theory and a computational analysis. Through a series of conversations and structured questions, the scope and context, which were largely implicit in the data, were specified, and a simulation was produced in a process akin to ‘rapid prototyping’. The aim was to produce the design for a simulation that included the key elements and behaviours identified from the qualitative data and as few other elements as possible. This paper describes this process, the CSNE framework, as well as the simulation that resulted. The lessons learned for such an exercise are reported.
SC-FDMA based Secure Data Transfer

In today's world of science exchange of information in a secure manner is complex due to data breaching. This happens mainly in hospitals, banks and military. Data breach leads to very severe consequences. The data's security level should be high. In this manuscript cryptography and steganography is used to secure secret text. The poly alphabetic encryption and Least Significant Bit (LSB) based image steganography is used for encrypt the text and stash it in image respectively. Then SC-FDMA (Single carrier Frequency Division Multiple Access) has been used to transmit the image. SC FDMA based transceiver has been chosen due to its low Peak to Average Power Ratio (PAPR). The Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are used to analyze the original and stego image.