Reviewerly/exHarmony huggingface dataset:
The Functional Building Blocks of Emoji in Consumer Engagement

Firms are struggling with developing effective consumer engagement strategies on social media. Emoji have been identified as a potent tool for consumer engagement. Yet, their use in this context is not well understood. Treating emoji as digitally mediated visual signs, this study provides a framework of five functional building blocks of emoji in the consumer engagement domain (i.e., beatification, affection, information, participation, conversation), and offers emoji strategy recommendations (i.e., branding aesthetics, brand humanization, social listening, online community engagement). The proposed framework can advance visual semiotics theory by applying it in the context of social media, and also broaden emoji and consumer engagement literatures by providing conceptual support that addresses the symbolic significance of emoji in the complex and dynamic digital space. Utilised individually and together, these building blocks allow marketers to understand emoji as a new form of marketing semiotics, while also guiding them to enable and shape consumer engagement on social media.
V.I. Lenin State Library of the USSR during the Great Patriotic War: Preparation and Release of Bibliographic Publications

In the year of the 75th anniversary of the Great Victory, the authors consider the activity of the V.I. Lenin State Library of the USSR (GBL) on preparation and release of bibliographic publications during the Great Patriotic War. This topic was covered in the specialized literature briefly and fragmentally (with the exception of work in the field of recommendation bibliography). The aim of the article is to fill in the existing gaps in the study of this area of GBL activity during the war. In the pre-war years, the GBL transformed into one of the leading bibliographic centres of the country — both reference and bibliographing. Both directions were mainly concentrated in the Scientific Bibliographic Department, where at the turn of the 1930s and 1940s preparation of bibliographic publications was structurally distinguished. With the beginning of the war, the GBL reorganized its work in accordance with the conditions and tasks of wartime. The amount of bibliographic work in the first months of the war was significantly reduced. Publishing activities, including the production of bibliographic publications, ceased and resumed in the second half of 1942. Recommendation indexes and lists on actual subjects were the main type of bibliographic materials created and published by the GBL during the war years. The article highlights the activities of two leading bibliographic departments of the GBL during this period: the Scientific Bibliographic Department and the Military Department. There are two areas of activity of the Scientific Bibliographic Department: general and scientific-auxiliary bibliography and recommendation bibliography. During the Great Patriotic War, the library produced mainly reference manuals, as well as also prepared several scientific publications. The authors present employees of both departments who have made the most significant contribution to the preparation of bibliographic publications. In addition, the article notes several bibliographic publications created in other structural divisions.
Turbulence in the upper troposphere according to long-term satellite measurements and its relationship with climatic parameters

A method is described for determining the characteristics of turbulence zones in the upper troposphere based on the processing of measurement data of the SEVIRI radiometer of second-generation European geostationary meteorological satellites in a 6.2 μm water vapor channel.Their spatial and temporal variability in the time interval 2007-2018 is considered.A significant (by 130-60 %) increase in areas occupied by relatively weak and moderate turbulence and a decrease (by 6-33 %) in areas with strong and very strong turbulence were revealed.The relationship of turbulence zones with the characteristics of jet stream and the temperature of the troposphere is investigated.Significant with a probability of more than 95 % annual variations of area of turbulence zones and most of the characteristics of jet stream have been identified.The most pronounced are in phase annual variations of the turbulence areas with the jet stream area and the velocity gradient and counterphase variations with the latitude and longitude of the centre of the jet stream.The connection between the variability of turbulence and the temperature variability of the upper troposphere is revealed.In this case, the effect of temperature on turbulence manifests itself indirectly through the characteristics of jet stream.
Blockchain technology's applications and challenges: An overview

Blockchain emerges as a novel distributed digital ledger with consensus of a majority scheme for economic transaction. Recently, major implementations across different sectors that includes the education projects, technology projects, smart contracts, and supply chain management systems. In addition, any other digital data that needs to executed, stored, verified and continually update among participating parties with security, privacy, trust, and transparency without a central authority having control can be relying on Blockchain technology. Blockchain could be new revolution in information technology. This research aimed to conduct an overview of Blockchain attributes and its applications and challenges that encounter during the implementation. Our objective is exploring number of common Blockchain technology applications to highlight of how transparency, anonymity, tamper resistant, and secure decentrality helps to get rid security breaches compromising the current model, in which third parties collect and control massive amounts of critical data. In contrast, number of factor observed as issues that affect deploying Blockchain technology such as majority attack, fork problems, scale, and others issues. The future works must be concerned the proposed solutions in many real world verity environments and explore the characteristics, benefits, and evaluation their effectiveness.
Learning to Select, Track, and Generate for Data-to-Text

We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and remembers which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. In addition, we also explore the effectiveness of the writer information for generation. Experimental results show that our model outperforms existing models in all evaluation metrics even without writer information. Incorporating writer information further improves the performance, contributing to content planning and surface realization.
Spam Classification Based on Signed Network Analysis

Online social networking services have become the most important information-sharing medium of modern society due to several merits, such as creating opportunities to broaden social relations, easy and instant communication, and fast data propagation. These advantages, however, are being abused by malicious users to disseminate unsolicited spam messages, causing great harm to both users and service providers. To address this problem, numerous spam detection methods utilizing various spam characteristics have been proposed, but most of them suffer from several limitations. Using individual behaviors and the content of messages for spam classification has been revealed to have bounded performance, since attackers can easily fake them. Instead, exploitation of social-network-related features has been highlighted as an alternative solution, but recent spam attacks can adroitly avoid these methods by controlling their ranking through various forms of attack. In this paper, we delineate a signed-network-analysis-based spam classification method. Our key hypothesis is that the edge signs are highly likely to be determined by considering users’ social relationships, so there will be a substantial difference between the edge sign patterns of spammers and that of non-spammers. To identify our hypothesis, we employ two social psychological theories for signed networks—structural balance theory and social status theory—and the concept of surprise is adopted to quantitatively analyze the given network according to these theories. These surprise measurements are then used as the main features for spam classification. In addition, we develop a graph-converting method for applying our scheme to unsigned networks. Extensive experimental results with Twitter and Epinions datasets show that the proposed scheme obtains significant classification performance improvement compared to conventional schemes.
Modelling the Factors Influencing the Adoption of a Map-Based Disaster Application in Indonesia (SIKK Magelang): a PLS-SEM Approach

Indonesia has high exposure to natural hazards, frequent disaster events, and a highly vulnerable population. Hence, risk communication and disaster information dissemination are crucial to reducing risks. Numerous disaster mobile applications (apps) with location-based services or maps already exist for facilitating risk communication to the public; however, availability does not automatically translate into use or adoption. Therefore, to understand the factors influencing the adoption of a map-based disaster app, the factors that affect the adoption of a local government-initiated map-based disaster app in Indonesia, namely Sistem Informasi Kebencanaan Kabupaten Magelang, or Disaster Information System of Magelang Regency (SIKK Magelang), were examined through the theoretical lenses of a combined information system (IS) success model. Partial Least Squared – Structural Equation Modelling (PLS-SEM), with the help of Smart-PLS, was used to examine the hypothesized relationships among the proposed constructs (latent variables). Nine exogenous latent variables were proposed as the antecedents of intention to use a map-based disaster application (the endogenous latent variable), including information quality, system quality, familiarity with online maps, perceived usefulness, user satisfaction, facilitating conditions, risk perceptions, information seeking behavior, and perceived quality of the Internet connection. Information-seeking behavior was also predicted to be influenced by risk perception. A questionnaire survey with an app trial was conducted in Magelang Regency for data collection. The study results confirmed the applicability of the combined IS success model frameworks in predicting the adoption of this map-based disaster app by the public. Although new constructs representing disaster-related online maps and familiarity with those maps were initially proposed in the contextual model, the model validation results showed that intention to use SIKK Magelang was significantly influenced only by perceived usefulness and user satisfaction. Practically, this study’s results offer guidance to the local emergency managers in Indonesia, especially Magelang Regency, regarding how to develop strategies to increase the app’s adoption.
An Improved Reversible Data Hiding in Encrypted Image Method Based on Block Selection

For reversible data hiding in encrypted image (RDHEI), the most important problem is the conflict between reversibility and embedding capacity. Reversibility has been realized by existing methods, however, the embedding capacity is still too low for practical RDHEI applications. To settle this problem, an improved RDHEI method is proposed in this article by presenting a block selection mechanism. Since extraction error occurs on blocks from which the fluctuation function fails to identifies the flipped part, we propose to distinguish these exceptional blocks from extraction-correct-block (ECB) (i.e., pixel block from which the hidden data can be exactly extracted) and embed additional data into only ECBs. In this way the minimum block size can be adopted to pursue high capacity. To guarantee reversibility, a new self-embedding method is proposed to embed the auxiliary information into the cover image. Finally, a new fluctuation function is also proposed to provide more ECBs and thus further improve the embedding capacity. Experimental results show that the proposed method not only achieves significant improvement in capacity but also guarantees lossless data extraction and perfect image recovery.
An Innovative Approach to Solve Healthcare Issues Using Big Data Image Analytics

The increasing adoption of transmission of medical images through internet in healthcare has led to several security threats to patient medical information. Permitting quiet data to be in peril may prompt hopeless harm, ethically and truly to the patient. Accordingly, it is important to take measures to forestall illicit access and altering of clinical pictures. This requests reception of security components to guarantee three fundamental security administrations – classification, content-based legitimacy, and trustworthiness of clinical pictures traded in telemedicine applications. Right now, inside created symmetric key cryptographic capacities are utilized. Pictorial model-based perceptual image hash is used to provide content-based authentication for malicious tampering detection and localization. The presentation of the projected algorithm has been evaluated using performance metrics such as PSNR, normalized correlation, entropy, and histogram analysis, and the simulation results show that the security services have been achieved effectively.
Ontology Learning using Hybrid Machine Learning Algorithms for Disaster Risk Management

Disaster is inevitable but manageable thru careful planning, preparation and immediate response strategies. During typhoons, earthquakes and other calamities, agreement about language is vital to understand each other well to avoid high number of deaths, delay in access to basic needs and slow response time. However, some of the people involved in this domain find it hard to coordinate and respond to different emergency situations due to lack of familiarization and knowledge about the different terms or concepts. In disaster risk management, the consistency and reusability of the sharing of information is important to avoid possible risks. Due to this reason, an ontology is incorporated to aid in the disaster management process. The use of ontology enables quick retrieving and incorporating "consistent data" and information related to disaster management which plays an important for making decisions efficiently. This paper aims to implement and evaluate the accuracy of Support Vector Machine (SVM) and Neural Network (NN) learning-based ontology for disaster risk management to enhance the classification of concepts (keywords) generated for the domain ontology. The experiment shows that the hybrid SVM and NN machine learning algorithm outperformed the accuracy of SVM and NN based on the precision, recall and F-Measure criterion.
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning

Deep learning (DL) frameworks take advantage of GPUs to improve the speed of DL inference and training. Ideally, DL frameworks should be able to fully utilize the computation power of GPUs such that the running time depends on the amount of computation assigned to GPUs. Yet, we observe that in scheduling GPU tasks, existing DL frameworks suffer from inefficiencies such as large scheduling overhead and unnecessary serial execution. To this end, we propose Nimble, a DL execution engine that runs GPU tasks in parallel with minimal scheduling overhead. Nimble introduces a novel technique called ahead-of-time (AoT) scheduling. Here, the scheduling procedure finishes before executing the GPU kernel, thereby removing most of the scheduling overhead during run time. Furthermore, Nimble automatically parallelizes the execution of GPU tasks by exploiting multiple GPU streams in a single GPU. Evaluation on a variety of neural networks shows that compared to PyTorch, Nimble speeds up inference and training by up to 22.34$\times$ and 3.61$\times$, respectively. Moreover, Nimble outperforms state-of-the-art inference systems, TensorRT and TVM, by up to 2.81$\times$ and 1.70$\times$, respectively.
Analysis of body-induced thermal signatures for social distancing monitoring

Thermal vision systems based on low-cost infrared (IR) array sensors allow to track thermal signatures induced by moving people and are promising technologies for monitoring body temperatures as well as social distancing in critical congested areas. This paper proposes a Bayesian framework for joint recognition of body temperature and location (distance and direction of arrival) of people in an indoor operational environment. Unlike conventional frame-based methods, the proposed approach exploits a statistical model for the estimation of the body distance from the IR sensors and of the direction/angle of arrival (AOA) using the body-induced thermal signatures and a mobility model for tracking multi-body motions. Compared to conventional machine learning approaches, the proposed framework processes backlogs of thermal images to prevent typical detection problems such as subject disappearance. The Bayesian method for distancing monitoring is verified experimentally with field measurements for wall mounted IR sensors.
Driving Accident Detection by Self-Supervised Adversarial Appearance-Motion Prediction in First-Person Videos

Driving accident is the event that occur unexpectedly and should be detected effectively for autonomous driving systems. In this paper, we propose a method based on adversarial appearance-motion prediction for driving accident detection in dashcam videos. The novelty of this method is to consider the predictability of the frame-level and object-level motion and appearance from the current to the future. Through a self-supervised adversarial learning between the real observation in next frame and the predicted motion and appearance, the objects that may occur accident are detected. In order to evaluate the accuracy of the detection results of our method, we evaluate the performance on A3D dataset, and the effectiveness of the method is validated.
CSC-GAN: Cycle and Semantic Consistency for Dataset Augmentation

Image-to-image translation is a computer vision problem where a task learns a mapping from a source domain A to a target domain B using a training set. However, this translation is not always accurate, and during the translation process, relevant semantic information can deteriorate. To handle this problem, we propose a new cycle-consistent, adversarially trained image-to-image translation with a loss function that is constrained by semantic segmentation. This formulation encourages the model to preserve semantic information during the translation process. For this purpose, our loss function evaluates the accuracy of the synthetically generated image against a semantic segmentation model, previously trained. Reported results show that our proposed method can significantly increase the level of details in the synthetic images. We further demonstrate our method’s effectiveness by applying it as a dataset augmentation technique, for a minimal dataset, showing that it can improve the semantic segmentation accuracy.
Auxiliary Lexicon Word Prediction for Cross-Domain Word Segmentation

Recent work has explored various neural network-based methods for word segmentation and has achieved substantial progress mainly in in-domain scenarios. There remains, however, a problem of performance degradation on target domains for which labeled data is not available. A key issue in overcomming the problem is how to use linguistic resources in target domains, such as unlabeled data and lexicons, which can be collected or constructed more easily than fully-labeled data. In this work, we propose a novel method using unlabeled data and lexicons for cross-domain word segmentation. We introduce an auxiliary prediction task, Lexicon Word Prediction, into a character-based segmenter to identify occurrences of lexical entries in unlabeled sentences. The experiments demonstrate that the proposed method achieves accurate segmentation for various Japanese and Chinese domains.
Compact Multi-Party Confidential Transactions

“Confidential Transactions”, integrated transactions of commitments, signatures, and zero-knowledge range proofs, are favored for their ability to hide transaction amounts. In the real world, multi-party fund transfers are highly desirable for personal and business security. Unfortunately, existing unproven Multi-Party Confidential Transactions are linear in the (exact) number of co-owners; hence they are not compact, very scalable, nor private (leak number of users and their public information). In this study, we provide provably secure private, compact Multi-Party Confidential Transactions, in both the “unanimous” N-out-of-N and “threshold” T-out-of-N settings. Unlike other schemes, our multi-party transactions have the size of single-owner transactions and hide the number of participants. To the best of our knowledge, ours is the first proven secure multi-party and threshold confidential transaction protocol.
A Critical Review on Using Blockchain Technology in Education Domain

The capability of blockchain technology to record transactions as a distributed digital ledger has facilitated registration, confirmation, and sharing of contracts among legitimate parties in a secure manner. This facility enhances privacy and security in various domains including finance, asset management, Internet of Things, and healthcare. Such secure and decentralized ledgers provide an integrated and complete e-governance solution in various domains. Despite growing usage of blockchains for various applications, little is known about its usage in education domain. This book chapter aims at critically analyzing the existing techniques used in blockchains and their suitability in education domain. The advantages and challenges in using blockchain-based applications in education are also discussed in this paper. Security breaches and attacks on using blockchain technology are elaborated in this chapter along with possible countermeasures. The chapter concludes with a note on how existing blockchain models can be enhanced to develop effective applications in education domain.
A Survey on Prevention Techniques for Camcorder Video Piracy in Movie Theaters

Movies, advertisements, and promotional videos are Intellectual Property (IP) and are often shared on open access medium as part of the business. Fueled by the advancements in technology, piracy has become more rampant and a widely spread crime in many countries. Movie piracy is more dangerous to mega-budget producers who struggle to receive the maximum profit of their films, making them compete with smaller films, results in the cheapest alternatives, jeopardizing the production of more films, artistic pool, shooting sites, camera equipment, special effects, quality and much more. Apart from the big shrinking of the job market for film jobs, sponsorship, unions, and merchandising get affected greatly. Many advanced technologies, apart from watermarking, such as Infrared based techniques, are used to estimate the position of the pirate and reduce piracy. This paper reviews the enabling technologies in the fight against video piracy, focusing mainly on methods for stopping these emerging methods of piracy in theatres.