Reviewerly/exHarmony huggingface dataset:
Webinar as a Variation of Smart Technologies for Schoolteachers’ Advanced Training

Purpose: The authors of the article attempt to fill the gap in understanding the peculiarities of using smart technologies to improve the skills of specialists on the example of organizing webinars, as the challenges of modern society present new requirements for the quality and accessibility of education in the context of lifelong learning.Design/Methodology/Approach: The work uses cognitive-generalizing, diagnostic, observational, praximetric and mathematical research methods for, respectively, a theoretical analysis of modern scientific literature on the issue, foreign language teachers’ questioning, monitoring the progress of webinars and generalizing the own pedagogical experience as organizers of webinars, and for statistical processing of results.Findings: The experience of holding webinars on the base of Skype technology and on the Etutorium (Ettorium) platform at Volgograd State University, whose target audience is teachers and pedagogues of educational institutions, has demonstrated the undoubted advantages of this smart technology application for professional development. Webinar participants, in particular, have the opportunity to master the educational programme of interest without departing from the workflow and at the time convenient for them. In addition, the study shows the effectiveness of this technology in the distance learning of other categories of webinar participants, for instance, people with disabilities.Originality/Value: Applying webinars as a variation of smart technologies for schoolteachers’ advanced training as our experience shows is in demand, corresponds to the modern realities of technological progress, allows trainees to acquire the necessary skills in an interactive form, exchange content, receive feedback and develop educational products.
Attention U-Net with Active Contour Based Hybrid Loss for Brain Tumor Segmentation

Brain tumor (BT) segmentation from brain magnetic resonance imaging (MRI) plays an important role in diagnosis and treatment planning for patients. In this study, we proposed a new approach for brain tumor segmentation based on deep neural networks. The paper proposes to use Attention U-Net architecture which can handle the shape variety with the attention gate for brain tumor segmentation from MRI images. Especially, instead of using cross-entropy loss function, dice coefficient loss function or both, we propose to utilize a new loss function based on activate contour loss that is known to overcome the limitation of pixel-wise fitting of the segmentation map on the loss functions used before, to train the network. We evaluated and compared our approach and other approaches on a dataset of nearly 4000 brain MRI scans. Experiments demonstrate that the proposed method outperforms the state-of-the-art methods in terms of Dice coefficient and Jaccard indexes.
Swarm Intelligence Optimisation Algorithms and Their Applications in a Complex Layer-Egg Supply Chain

Optimising processes in a supply chain can benefit enormously all participating companies and the entire supply chain, e.g. for cost-cutting or profit raising. Traditionally, simulation-based technologies are used for such purposes. However, such methods can be expensive and under-performing when the solution space is too large to be adequately explored. Biologically inspired computing approaches such as swarm intelligence algorithms are uniquely suited to solve complex, exponential, vectorial problems, such as those posed by multi-product supply chains connected with a large and diverse customer base and transportation methods. Although swarm intelligence algorithms have been used to optimise supply chains before, there has been little work on formalising and optimising the layer-egg supply chain, or the supply chain of a perishable product—where same/similar products can be packaged to form different product offerings to seek optimised configurations for different buyers based on different pricing and cost structures. In this paper, we introduce two new Swarm Intelligence algorithms and use them to optimise the profits of participating suppliers in a real-world layer-egg supply chain using its operational data, trade network, and demand & supply models. Several swarm intelligence algorithms were discussed and their performance was compared. Through this, we aim to understand how the complex domain of real-life layer-egg supply chain may be suitably formalised and optimised in a bid to help improve and sustain layer-egg supply chain's financial well-beings.
The AI enabled Chatbot Framework for Intelligent Citizen-Government Interaction for Delivery of Services

Semantic modeling of domain knowledge for natural language question-answering based on conversational assistants nowadays has gained popularity with the development of chatbots and voice assistants. Chatbots based on artificial intelligence and interactive technologies are helping organizations and governments to interact and accomplish tasks such as question answering, instant messaging, and promoting any ideas or services. As various government services are provided to fellow citizens through manual interaction or through electronic means. This causes a huge burden of works on various government departments and many queries of the citizens remain unresolved. Nowadays, intelligent chatbots can mimic humans and help users who are not acquainted with technologies to avail answers to their domain-specific queries. However, providing answers with domain-specific capabilities still remain a challenge. In this paper, we present the design of a conversational assistant for answering user queries and administrative support. It is developed using Google Dialogflow and trained on a domain-specific semantic model with intelligent abilities to answer user queries and process service requests. On analysis, we observed that the developed chatbot performs with approx. 95% accuracy in responding to the queries. The chatbot has been deployed in NIC for assistance to user queries regarding e-District services.
A Novel Channel Attention Mechanism for Human Action Recognition Based on Convolutional Kernel

Abstract With the improvements of computer performance, deep learning has gradually expanded from 2D image tasks to 3D video tasks. Human action recognition is a typical 3D video task, which can achieve category classification by capturing human action characteristics. However, most of the videos are processed by encoding and decoding technology at current, thus the motion details are blurry, which makes it difficult for human action recognition. To solve this problem, we utilize the attention mechanism to “ignore” the blurred feature caused by video coding and decoding technology. Therefore, we hope to embed the attention mechanism in 3D spatiotemporal CNN to overcome this problem. Compared with 3D CNN, the effectiveness of our method is verified on UCF101 and HMDB51 dataset. And our method also proves the convolutional kernel implies channel-wise dependence. Although the improvement of the proposed method is limited, we hope the channel attention mechanism can help people to study neural network.
Deep Unsupervised Hashing by Distilled Smooth Guidance

Hashing has been widely used in approximate nearest neighbor search recently. Deep supervised hashing methods are not widely-used because of the lack of labeled data, especially when the domain is transferred. Meanwhile, unsupervised deep hashing models can hardly achieve satisfactory performance due to the lack of reliable similarity signals. Here, we propose a novel deep unsupervised hashing method, namely Distilled Smooth Guidance (DSG), which can learn a distilled dataset consisting of similarity signals as well as smooth confidence signals. Specifically, we obtain the similarity confidence weights based on the initial noisy similarity signals learned from local structures and construct a priority loss function for smooth similarity-preserving learning. Besides, global information based on clustering is utilized to distill the image pairs by removing contradictory similarity signals. Extensive experiments on three widely used bench-mark datasets show that the proposed DSG consistently out-performs the state-of-the-art search methods.
Adaptive algorithms of error correction and error estimation in quantum cryptography

Quantum cryptography provides methods for secret key distribution between the two distant users. An important step in quantum key distribution protocols is the correction of errors that occur during the transmission and implementation of the protocol. Here we consider error estimation and error correction with linear codes such as repetition code, Hamming code, low density parity check (LDPC) code. The paper investigates the increase in the efficiency of error correction using a combination of a repetition code and a LDPC code for a high error probability.
Application of the User’s Digital Footprint in the Adaptive Authentication Problem

This paper describes the application of adaptive user authentication based on a calculated digital footprint. The digital footprint is considered as an integrated indicator that the user is unique in its set of characteristics and behavior on the network. The main classes of digital footprint sources are marked, with the greatest attention paid to browser fingerprints. Using a mathematically supported method based on the comparison of the digital footprint, an experiment is conducted to determine the authenticity of the user in the three cases under consideration. The experiment showed that the method works correctly and successfully and can give better results by optimizing its parameters: the weights of criteria, the weights of browser fingerprints and the threshold value of user's uniqueness.
Geographic media literacy for indonesian geography education

Abstract In this information-saturated world, people are thriving to cope with seemingly overload streams of information and images. The digital advancements alter and extend our means of communication abilities, information access, and spatial formations. Hence, a new literacy is needed to wield this new digital world. In this regard, the current conceptual paper attempts to explore the nexus of media literacy and geography education in coping with the digital era. The paper reviews the literature on geographic media literacy by describing the essential elements of the relevant literacy coined by Lukinbeal (2008). The paper goes on to discuss the three factors that play a prominent role in geographic media literacy. Finally, this paper recalls the importance of geographic media literacy and how it can contribute to the development of geography education in the Indonesian Setting.
Analysis of the Management System for the Balanced Innovative Development of Agricultural Production

The tendencies of new technological paradigms dictate the need for socio-economic and environmental stability, and for innovative economic growth. This may provide a faster way of overcoming the current regional crises. The present rapid digitalization of Russian economy brings attention to forming a balanced, innovative development of the agricultural sector, which largely affects the living standards of the rural population, food security, and general national security. These conditions emphasize the importance of forming an effective management system for innovative agricultural development. Developing the infrastructure would increase the return on investment of innovative development in the agro-industrail complex. In this research, we specify the main stages of the management system for the balanced innovative agricultural development. The paper establishes the principles of balanced development in the beginning, middle, and end phases. The study describes structural and logical patterns of the interrelations between the agro-industrail complex, nature, and society. This study may prove useful to the regional and federal authorities, and researchers involved in the development of the regional agricultural sector.
DFILN: Deep Feature-interactive Learning Network for Object Detection

This paper puts forward a novel object detection method through utilizing deep features learned in high-level layers. Compared with features yielded in shallow layers, the deep features are better at expressing semantic and contextual information. The proposed deep feature-interactive learning convolutional neural network (DFILN) efficiently detects the objects via introducing feature-interactive learning blocks into the high-level layers. And it considers not only individual objects and local contexts but also their relationships. Our proposed DFILN with a relatively small backbone (VGG16) keeps the real-time speed while reaching the performance of advanced detectors. It outperforms baseline SSD by 1.32 point on PASCAL VOC2007 testset, especially with 2∼7 points improvement on some small objects categories.
Book-Share (BS) Apps using the Android System

Abstract Information technologies including information systems play an important role to support education systems specifically in remote area such as Papua. However, based on World’s most literate Nations (WMLN) study found that Indonesia is the second-least literate nation in the World. This study focuses on the empowering of open source android software to facilitate students or teachers to borrow, share or exchange books. The system consists of two modules, i.e. the Backend as a Service (BaaS) is obtained through firebase web service, and client android system. The findings may be helpful for both borrowers and owners using this application in selecting, borrowing, reviewing, applying, adding book profile, and viewing. To end this, the application also able in displaying maps and routes, private messages as well as notification.
Image Feature Fusion and Fisher Coding based Method for CBIR

The paper proposed a new method for content-based image retrieval (CBIR) based on image feature fusion and fisher encoding (FV). Firstly, low-level image content features such as hue-saturation-value (HSV) histogram, uniform local binary patterns (LBP), Dual-Tree complex wavelet transform (DTCWT) are extracted based on image blocks. In contrast, high-level features are extracted by using the AlexNet convolutional neural network (CNN). The singular value decomposition (SVD) was applied to the LBP and DTCWT. Secondly, low-level features are fused using normalization and weights. Lastly, after using the FV encoding, the fused fisher vectors are used to measure the similarity of image pairs. The experimental results on the benchmark Corel-1k show that the accuracy on the top 10, 12, and 20 images returned are 93.4%, 92.8%, and 91.4%, respectively.
Re-structuring library resources and services in IIT Delhi library: analytical study from users' perspective

Purpose This study aims to assess users' satisfaction from the re-structured library resources and services due to technological innovations in the Indian Institute of Technology Delhi library. It also identifies and suggests potential improvement areas. Design/methodology/approach The study adopts a survey research method to collect data from the end-users of the library. Data were collected through a structured questionnaire. In total, 100 copies of questionnaires were distributed to the students, and out of the filled received questionnaires, 92 were found dully completed and useable. Collected data were analyzed by using percentage and chi-square test for testing of hypothesis. Findings This study shows that users' usage and preference for e-resources and e-services are increasing as there is a general trend to overlook traditional print resources where e-resources are available in the first place. The study recommends that a radical rethinking is required to strengthen the electronic collection and improve the library services. Practical implications The paper has a direct practical implication. It suggests that as technology continues to evolve, it is likely that academic institutes find themselves identifying appropriate areas to redesign library activities and services regularly. This paper reveals the practice of re-engineering in academic library services and knowing students' specific preferences of resources and services to enhance library usage. Originality/value The study is based on the self-designed structured questionnaire. Primary data has been collected from the IIT Delhi library users, thus providing original information and informative inputs to the existing literature.
Assessment of appropriate areas for the establishment of Solar Thermal Power Plant in Rajasthan

Abstract In past years, cost of photovoltaic has decreased and investments of solar power plant have increased. In spite of this, practical studies remain paramount as cost of installation is comparatively high. One of pivotal parameters in studies is the proper location selection. So, in this paper we mix Analytical hierarchy process (AHP) and multi-criteria decision making method (MCDM) to get the results of the ideal position of sunlight based energy station in Jodhpur, Rajasthan. Using MCDM the models or factors referenced will be weighted to assess possible destinations to find a sunlight based plant. Examination and count of the loads of these variables will be directed utilizing AHP. For this study Sardarpura, Kabir Nagar and Mandore are selected as examples. These places are located in same plane. The decision of such measures is set apart by the impact introduced to the general objective; for this situation in climatic models and geomorphological. In study, it is found that Kabir Nagar is ideal position for establishment of solar power plant. This result is based on criteria, feeder capacities, solar energy potential and earth slopes. The result are ratified by considering the total amount of area and sunshine duration.
Research on the Influence of Network Distance Teaching on the Reform of College Physical Education

Abstract With the development of computer technology, higher education reforms have made better progress. After the application of computer technology is mature, colleges and universities begin to use computer network distance courses to expand students’ learning resources and learning horizons. This paper mainly focuses on the application and reform influence of network distance teaching in physical education.
TAP: A Transformer based Activity Prediction Exploiting Temporal Relations in Collaborative Tasks

Activity prediction is an important challenge to provide cognitive supports in a smart space. For user activity prediction without privacy and inconsistent data collection issues, recent studies leverage data stream generated by ambient sensors. They use a deep learning model for single-user tasks which only contain simple sequential relations between activities. In this paper, we propose TAP, a Transformer-based activity prediction approach for inferring the next activity in a collaborative smart environment where collaborative tasks are conducted. To represent and analyze complex relations between activities of users, Allen's temporal relations are employed for representing temporal relations between activities and we leverage the Transformer to predict not only the next activity but also temporal relation with its preceding activity. TAP yields higher accuracy for the next activity prediction and temporal relations with the current activity in a collaborative task than existing approaches.
Trust Evaluation of Ontological Decision Support Systems for Avionics Analytics

Artificial Intelligence (AI), including Machine Learning (ML), has gained prominence to perform data analytics in different application domains such as medicine, government, and avionics. The main challenge for AI/ML-based systems adoption in avionics requires trust the technology to support or carry out given tasks, e.g., decision making. This paper discusses a method to assess trustability in an ontological Decision Support System (DSS) for avionics analytics in Air Traffic Management (ATM). The approach leverages the Multisource AI Scorecard Table (MAST) which provides aviation stakeholders (e.g., developers and users) with a standard checklist. The MAST checklist is aligned with the principles of good analysis adopted by the Intelligence Community (IC) which helps promote the development of more understandable system to engender trust in AI outputs. Such a scorecard provides information-guided assessment of transparent and consistent understanding of AI/ML tools applied for commercial and government aviation use. This paper explores the application of the MAST to a DSS driven by an Avionics Analytics Ontology (AAO). It shows how a AAO-based DSS can be assessed (as to trustability) by means of a case study with two application scenarios set by different airspace situations for autonomy and collision avoidance.
Static and Dynamic Activities Prediction of Human Using Machine and Deep Learning Models

Recent advancement in smart phones and computing technologies has played a vital role in people’s life. Develop a model to detect the human basic dynamic activities such as Amble, Climb stairs, coming down the stairs into the floor and human basic static activities like Sitting, Standing or Laying using the person’s smart phone and computers are the major work of this paper. Conventional Machine learning models like Logistic Regression, SVC, Decision tree, etc. results are compared with a recurrent deep neural network model named as Long Short Term Memory (LSTM). LSTM is proposed to detect the human behavior based on Human Activity Recognition (HAR) dataset. The data is monitored and recorded with the aid of sensors like accelerometer and Gyroscope in the user smart phone. HAR dataset is collected from 30 persons, performing different activities with a smart phone to their waists. The testing of the model is evaluated with respect to accuracy and efficiency. The designed activity recognition system can be manipulated in other activities like predicting abnormal human actions, disease by human actions, etc. The overall accuracy has improved to 95.40%.
Method for Analyzing the Corrective Ability of Residue Number System

Residue Number System (RNS) is a non-positional number system that allows the construction of error-correcting codes. Features such as parallel processing capability and scalability are significant advantages of RNS. The redundancy of the RNS code allows the creation of error correction codes, affects their properties and can be selected for specific tasks. This paper describes a method for analyzing the distribution of errors over the entire range of the RNS. This method can improve the design of specific data storage and processing systems, since it allows determining the corrective properties of the used RNS.
Staking Assets Management on Blockchains: Vision and Roadmap

This paper introduces and explores the vision wherefore stakeholders and the process of staking —that is, the idea of guaranteeing the quality of a process by risking valuable assets on their correct execution— may run both on and off a blockchain while in the context of cloud-enabled services and processes. The emerging trend behind blockchain-oriented computing and the reliance on stakeholders therein make distilling and evaluating this vision a priority to deliver high-quality, sustainable services of the future. We identify key defining concepts of stakeholders and the staking process, using three very different staking scenarios as a base. Subsequently, we analyze the key challenges that these stakeholders face and propose the development of a framework that can help overcome these challenges. Finally, we give a road-map to steer systematic research stemming from the proposed vision, leveraging design science along with short-cyclic experimentation.