Reviewerly/exHarmony huggingface dataset:
A wink and a nod: The role of emojis in forming digital communities

Abstract As digital interactions become more global, individuals who bring divergent practices ‘to the keyboard’ must interact with other participants who come to the digital space with different cultural norms and expectations. This study explores the interface between local expectations and global practice through emoji use in online gaming – a venue which brings people from around the globe together on a common ‘playing field’. Since emojis were originally designed to tap into universals in human experience and expression, they are a ready-made resource through which individuals can integrate their culture-based expectations with communicative norms that are rooted in the common denominators of the (global) digital environment. Using live chat data from the game streaming platform Twitch, this study examines emojis posted to the open chat room during game streams of one female and one male gamer. The analysis examines the ways that participants use these semiotic images to orient toward gaming communities of practice and claim identities within gaming groups. It also explores whether emoji use is affected by the gender of the streamer. Analysis indicates that participants in the man’s stream differ from participants woman’s stream in the ways they use emojis to claim community membership and employ emojis as phatic devices.
Design and security analysis of two robust keyed hash functions based on chaotic neural networks

In this paper, we designed, implemented, and analyzed the performance, in terms of security and speed, of two proposed keyed Chaotic Neural Network (CNN) hash functions based on Merkle–D$$\mathring{\rm a}$$mgard (MD) construction with three output schemes: CNN-Matyas–Meyer–Oseas, Modified CNN-Matyas–Meyer–Oseas, and CNN-Miyaguchi–Preneel. The first hash function’s structure is composed of two-layer chaotic neural network while the structure of the second hash function is formed of one-layer chaotic neural network followed by non-linear layer functions. The obtained results of several statistical tests and cryptanalytic analysis highlight the robustness of the proposed keyed CNN hash functions, which is fundamentally due to the strong non-linearity of both the chaotic systems and the neural networks. The comparison of the performance analysis with some chaos-based hash functions of the literature and with standard hash functions make the proposed hash functions suitable for data integrity, message authentication, and digital signature applications.
Agent Based Information Security Framework for Hybrid Cloud Computing

In general, an information security approach estimates the risk, where the risk is to occur due to an unusual event, and the associated consequences for cloud organization.Information Security and Risk Management (ISRA) practices vary among cloud organizations and disciplines.There are several approaches to compare existing risk management methods for cloud organizations but their scope is limited considering stereo type criteria, rather than developing an agent based task that considers all aspects of the associated risk.It is the lack of considering all existing renowned risk management frameworks, their proper comparison, and agent techniques that motivates this research.This paper proposes Agent Based Information Security Framework for Hybrid Cloud Computing as an all-inclusive method including cloud related methods to review and compare existing different renowned methods for cloud computing risk issues and by adding new tasks from surveyed methods.The concepts of software agent and intelligent agent have been introduced that fetch/collect accurate information used in framework and to develop a decision system that facilitates the organization to take decision against threat agent on the basis of information provided by the security agents.The scope of this research primarily considers risk assessment methods that focus on assets, potential threats, vulnerabilities and their associated measures to calculate consequences.After in-depth comparison of renowned ISRA methods with ABISF, we have found that ISO/IEC 27005:2011 is the most appropriate approach among existing ISRA methods.The proposed framework was implemented using fuzzy inference system based upon fuzzy set theory, and MATLAB® fuzzy logic rules were used to test the framework.The fuzzy results confirm that proposed framework could be used for information security in cloud computing environment.
A generic and efficient emotion-driven approach toward personalized assessment and adaptation in serious games

Emotions have a major role in the player-game interaction. In serious games playing contexts, real-time assessment of the player’s emotional state is crucially important to enable an emotion-driven adaptation during gameplay. In addition, a personalized assessment and adaptation based on the player's characteristics remains a challenge for serious games designers. This paper presents a generic and efficient emotion-driven approach for personalized assessment and adaptation in serious games, in which two main methods and their algorithms are proposed. The first one is a method for assessing, in real time, the player's emotion taking into account the personality type and the playing style of the player. The second one is an emotion-driven personalized adaptation method based on Markov modeling of dependency between the serious game events and the change in the player's emotional state. Therefore, the proposed approach has been evaluated by playing an affective vs. non-affective version of a serious game that we have developed to illustrate the applicability of the above-mentioned methods. The overall results showed that owing to our approach, a serious game become able to enhance its adaptivity toward playing outcomes and improve its overall playability.
Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme

Global solar radiation prediction is highly desirable for multiple energy applications, such as energy production and sustainability, solar energy systems management, and lighting tasks for home use and recreational purposes. This research work designs a new approach and investigates the capability of novel data intelligent models based on the self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm to predict daily solar radiation in the Burkina Faso region. Four different meteorological stations are tested in the modeling process: Boromo, Dori, Gaoua and Po, located in West Africa. Various climate variables associated with the changes in solar radiation are utilized as the exploratory predictor variables through different input combinations used in the intelligent model (maximum and minimum air temperatures and humidity, wind speed, evaporation and vapor pressure deficits). The input combinations are then constructed based on the magnitude of the Pearson correlation coefficient computed between the predictors and the predictand, as a baseline method to determine the similarity between the predictors and the target variable. The results of the four tested meteorological stations show consistent findings, where the incorporation of all climate variables seemed to generate data intelligent models that performs with best prediction accuracy. A closer examination showed that the tested sites, Boromo, Dori, Gaoua and Po, attained the best performance result in the testing phase, with a root mean square error and a mean absolute error (RMSE-MAE [MJ/m2]) equating to about (0.72-0.54), (2.57-1.99), (0.88-0.65) and (1.17-0.86), respectively. In general, the proposed data intelligent models provide an excellent modeling strategy for solar radiation prediction, particularly over the Burkina Faso region in Western Africa. This study offers implications for solar energy exploration and energy management in data sparse regions.
A Container-Based Technique to Improve Virtual Machine Migration in Cloud Computing

Hypervisor-enabled virtualization is a promising technology to deploy applications and implement Virtual Machine (VM) migration on cloud data centers. However, one critical aspect in hypervisor-based solution is that to deploy applications each VM is installed with a dedicated guest OS along with the requirement of binaries and library files and substantially large VM's image size. Therefore, it is a significant concern for data center administrators because it causes performance overheads. To solve these issues, a new paradigm of virtualization technology that has attracted considerable attention is containerization. Although, progress has been made to address container-based virtualization, there has been less attention to explore the issue of performance evaluation of container and VM in a migration environment. The main contribution of this research study is to develop an experimental setup to implement and evaluate the performance of LXD/CR container-based migration technique and compared it with the pre-copy VM migration scheme. To conduct this performance evaluation, first we modified Linux kernel and implemented LXD which uses liblxc to launch container, then migration of the running container is facilitated using a checkpoint/restore mechanism of CRIU technique. To ensure the applicability and validity for real-time cloud infrastructure, we executed a wide variety of benchmarks such as web server, CPU-intensive, disk I/O and database. From the results obtained, it is found that compared to pre-copy VM migration scheme, LXD/CR container migration technique reduces the downtime, migration time, amount of data transferred and the number of pages transferred by 75.66%, 65.55%, 76.63%, and 76.78%, respectively. In addition, the migration overhead caused is reduced by 55.89% w.r.t. CPU utilization and 76.52% w.r.t. RAM utilization. This being an important contribution for migrating the running applications efficiently and consequently increase the business value of the cloud infrastructure providers.
The Emergence of Abstract Sciences and Transdisciplinary Advances: Developments in Systems, Man, and Cybernetics

Transdisciplinary studies in systems, man, and cybernetics (SMC) are an advanced approach to generate new knowledge and novel perceptions on persistent challenges and emerging technologies across the edges of traditional disciplines. An unprecedented phenomenon in science history in the past decade is the emergence of abstract sciences (ASs) as a counterpart of classic concrete sciences (CSs). It leads to a new perception of SMC as well as its transdisciplinary foundations and the impacts on classic sciences and engineering disciplines. This article presents the emergence of AS underpinned by SMC and denotational mathematics (DMs). It explores the transdisciplinary theories of system science, cognitive cybernetic foundations of ASs, and hybrid human-machine societies driven by the fast development of artificial intelligence (AI), intelligence science, and knowledge science as well as their engineering applications.
Weakly Supervised Salient Object Detection by Learning A Classifier-Driven Map Generator

Top-down saliency detection aims to highlight the regions of a specific object category, and typically relies on pixel-wise annotated training data. In this paper, we address the high cost of collecting such training data by a weakly supervised approach to object saliency detection, where only image-level labels, indicating the presence or absence of a target object in an image, are available. The proposed framework is composed of two collaborative CNN modules, an image-level classifier and a pixel-level map generator. While the former distinguishes images with objects of interest from the rest, the latter is learned to generate saliency maps by which the images masked by the maps can be better predicted by the former. In addition to the top-down guidance from class labels, the map generator is derived by also exploring other cues, including the background prior, superpixel- and object proposal-based evidence. The background prior is introduced to reduce false positives. Evidence from superpixels helps preserve sharp object boundaries. The clue from object proposals improves the integrity of highlighted objects. These different types of cues greatly regularize the training process and reduces the risk of overfitting, which happens frequently when learning CNN models with few training data. Experiments show that our method achieves superior results, even outperforming fully supervised methods.
Cross-lingual Transfer Learning for

Andrew Johnson, Penny Karanasou, Judith Gaspers, Dietrich Klakow. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
Redesigning initial teacher’s education practices with learning scenarios

Purpose The construction of learning scenarios is a way to plan for teaching activities, promoting the development of skills related to problem solving, collaboration, critical thinking and creativity. Using learning scenarios as a lesson planning strategy becomes a powerful tool in initial teacher education. On the one hand, it mobilizes teaching-related scientific concepts, and on the other hand, it offers opportunities to think on innovative pedagogic approaches involving strategies and capacities essential for the future teacher. Research shows that teacher education programs within real school contexts enriched with digital technologies represent an important factor in increasing the quality of teachers’ preparation and their future professional practice. The paper aims to discuss these issues. Design/methodology/approach The authors present the analysis of practice of design and implementation of learning scenarios in teachers’ initial education courses developed with students of teaching master degrees. Activity theory is used in the analysis of a case study of a student-teacher in Computer Science. Findings The results have been analyzed, contributing to the specification of the principles underlying the learning scenarios in initial teacher education. Research limitations/implications Results show the affordances and possibilities of using learning scenarios as structuring resources for the initial teacher education practice. Originality/value Therefore, the use of learning scenarios brings a set of potentialities to teacher training given its prospective nature.
A Blockchain-based Value Added Tax (VAT) System: Saudi Arabia as a Use-Case

Businesses need trust to confidently perform trade among each other.Centralized business models are the only mature solutions available to perform trades over the Internet.However, they have many problems which includes but are not limited to the fact that these create bottleneck on the server as well as requires trusted third parties.Recently, decentralized solutions have gained significant popularity and acceptance for future businesses.The wide acceptance of such systems is indeed due to the trust management among various untrusted business stakeholders.Many solutions have been proposed in this regard to provide de-centralized infrastructure for various business models.A standard solution that is acceptable to the industry is still in demand.Hyperledger umbrella Blockchain projects, that are supported by IBM and many other industry big players are gaining popularity due to its efficient and pluggable design.In this study, the author present the idea of utilizing Blockchain to design a Value-Added Tax (VAT) system for Saudi Arabia's newly introduced tax system.The reason to select this business model for VAT is twofold.First, it provides an untampered distributed ledger, which cannot be deceived by any party.Each transaction in the system cannot go unnoticed by the smart contract.Secondly, it provides a transparent record, and updates all involved parties regarding each activity performed by stakeholders.The newly proposed system will provide a transparent database of VAT transactions according to our smart contract design and at each stage of supply chain, tax will be deducted and stored on peer-to-peer network via consensus process.The author believes that the proposed solution will have significant impact on VAT collection in the Kingdom of Saudi Arabia.
Size-free generalization bounds for convolutional neural networks

We prove bounds on the generalization error of convolutional networks. Thebounds are in terms of the training loss, the number of parameters, theLipschitz constant of the loss and the distance from the weights to the initialweights. They are independent of the number of pixels in the input, and theheight and width of hidden feature maps. We present experiments with CIFAR-10and a scaled-down variant, along with varying hyperparameters of a deepconvolutional network, comparing our bounds with practical generalization gaps.
Semi-Supervised DeepMachine Learning Assisted Seismic Image Segmentation and Stratigraphic Sequence Interpretation

Summary Geological / geophysical interpretation of seismic survey commonly requires segmenting a seismic image into different layers/sequences, highlighting certain geobodies, or picking different horizon surfaces, for multiple purposes including, but not limited to, earth model building, velocity model building, stratigraphic analysis, etc. The traditional approach requires the interpreter significant amount of effort to interact with computer and label the data. We demonstrated an innovative workflow for seismic image/sequence/geobody segmentation and horizon picking, where a key aspect is that, it requires much less labels and hence significantly reduce interpreter&apos;s workload.
Improving Cuneiform Language Identification with

We describe the systems developed by the National Research Council Canada for the Cuneiform Language Identification (CLI) shared task at the 2019 VarDial evaluation campaign. We compare a state-of-the-art baseline relying on character n-grams and a traditional statistical classifier, a voting ensemble of classifiers, and a deep learning approach using a Transformer network. We describe how these systems were trained, and analyze the impact of some preprocessing and model estimation decisions. The deep neural network achieved 77% accuracy on the test data, which turned out to be the best performance at the CLI evaluation, establishing a new state-of-the-art for cuneiform language identification.
Trustworthy and profit: A new value-based neighbor selection method in recommender systems under shilling attacks

User-based collaborative filtering recommender systems are widely deployed by e-retailers to facilitate customer’ decision-making and enhance e-retailers' profitability. Despite the advantages these systems provide, their recommendation effectiveness is vulnerable to attacks from malicious users who inject biased ratings. Such attacks against recommender systems are called shilling attacks. Although several shilling attack detection mechanisms have been proposed in previous studies, their detection performance is limited in various attack conditions. Furthermore, few of these mechanisms consider the value-dimension associated with recommendations, which is crucial for e-retailers. This research proposes a novel approach called Value-based Neighbor Selection (VNS) to address the above limitations. The objective of this approach is to protect recommender systems from shilling attacks while improving e-retailers' profitability. It alleviates the aforementioned problems through strategically selecting neighbors whose preferences are then used to make recommendations. We have performed a series of empirical validations in various attack conditions to compare the performance of the proposed method and three benchmark methods, in terms of both recommendation accuracy and e-retailer profitability. The results show the advantages of the proposed method in balancing customer satisfaction and e-retailer profitability.
Exploring the Task Cooperation in Multi-goal Visual Navigation

Learning to adapt to a series of different goals in visual navigation is challenging. In this work, we present a model-embedded actor-critic architecture for the multi-goal visual navigation task. To enhance the task cooperation in multi-goal learning, we introduce two new designs to the reinforcement learning scheme: inverse dynamics model (InvDM) and multi-goal co-learning (MgCl). Specifically, InvDM is proposed to capture the navigation-relevant association between state and goal, and provide additional training signals to relieve the sparse reward issue. MgCl aims at improving the sample efficiency and supports the agent to learn from unintentional positive experiences. Extensive results on the interactive platform AI2-THOR demonstrate that the proposed method converges faster than state-of-the-art methods while producing more direct routes to navigate to the goal. The video demonstration is available at: https://youtube.com/channel/UCtpTMOsctt3yPzXqe_JMD3w/videos.
Quantitative causal selection patterns in token causation

When many events contributed to an outcome, people consistently judge some more causal than others, based in part on the prior probabilities of those events. For instance, when a tree bursts into flames, people judge the lightning strike more of a cause than the presence of oxygen in the air—in part because oxygen is so common, and lightning strikes are so rare. These effects, which play a major role in several prominent theories of token causation, have largely been studied through qualitative manipulations of the prior probabilities. Yet, there is good reason to think that people's causal judgments are on a continuum—and relatively little is known about how these judgments vary quantitatively as the prior probabilities change. In this paper, we measure people's causal judgment across parametric manipulations of the prior probabilities of antecedent events. Our experiments replicate previous qualitative findings, and also reveal several novel patterns that are not well-described by existing theories.
Eye Behavior During Multiple Object Tracking and Multiple Identity Tracking

We review all published eye-tracking studies to date that have used eye movements to examine multiple object (MOT) or multiple identity tracking (MIT). In both tasks, observers dynamically track multiple moving objects. In MOT the objects are identical, whereas in MIT they have distinct identities. In MOT, observers prefer to fixate on blank space, which is often the center of gravity formed by the moving targets (centroid). In contrast, in MIT observers have a strong preference for the target-switching strategy, presumably to refresh and maintain identity-location bindings for the targets. To account for the qualitative differences between MOT and MIT, two mechanisms have been posited, a position tracking (MOT) and an identity tracking (MOT & MIT) mechanism. Eye-tracking studies of MOT have also demonstrated that observers execute rescue saccades toward targets in danger of becoming occluded or are about to change direction after a collision. Crowding attracts the eyes close to it in order to increase visual acuity for the crowded objects to prevent target loss. It is suggested that future studies should concentrate more on MIT, as MIT more closely resembles tracking in the real world.
Inclusive Museums and Augmented Reality: Affordances, Participation, Ethics, and Fun

Augmented reality (AR) technology possesses several affordances that can support disabled museum visitors. A structured research review was used to examine the ways in which AR being used in, or developed for, museums to support access for disabled people and /or those with cognitive or sensory impairments. It also considers the extent to which the underpinning research approaches are inclusive. The findings suggest that AR can positively transform aspects of disabled users’ museums experiences. However, disabled people’s involvement in AR research, intended for use by them, appears problematic.