Reviewerly/exHarmony huggingface dataset:
Quantum Cryptography for Nuclear Command and Control

The nuclear inventory of Russia and the USA currently comprises 12,685 warheads in a large network of vehicles; and the interconnected network is managed by a command and control communication system. This command and control communication system (C3) must also relay information from numerous airborne, space-born, and ground sensors throughout the network in potentially degraded environments and are nonetheless meant to securely hold transmissions that must be held to the highest standards of encryption. C3 systems are also arguably one of the most challenging systems to develop, since they require far more security, reliability, and hardening compared to typical communication systems, because they typically must (absolutely) work while other systems fail. Systems used for C3 are not always cutting-edge technology, but they must be upgraded at crucial junctures to keep them at peak performance. This manuscript outlines a blueprint of a way to embed current and future systems with revolutionary encryption technology. This will transform the security of the information we pass to our C3 assets adding redundancy, flexibility, and enhanced speed and insure vehicles and personnel in the system receive network message traffic. Quantum key distribution (QKD) has the potential to provide nearly impregnable secure transmissions, increased bandwidth, and additional redundancy for command and control communication (C3). While QKD is still in its adolescence, how QKD should be used or C3 must be charted out before it can be engineered, tested, and implemented for operations. Following a description QKD functionality, its pros and cons, we theorize the best implementation of a QKD system for C3.
AUTOMATIC ASSESSMENT TO ENHANCE ONLINE DICTIONARIES CONSULTATION SKILLS

We live in a digital world and our students are surrounded by technologies: it is essential that they learn how to use thedigital tools available for education purposes. As for language teaching, among the most recent technologies we can
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting

Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature as the number of population can vary in $$[0,+\infty )$$ in theory. However, collected data are limited in reality, which means that only a closed set is observed. Existing methods typically model this task through regression, while they are prone to suffer from unseen scenes with counts out of the scope of the closed set. In fact, counting has an interesting and exclusive property—spatially decomposable. A dense region can always be divided until sub-region counts are within the previously observed closed set. We therefore introduce the idea of spatial divide-and-conquer (S-DC) that transforms open-set counting into a closed set problem. This idea is implemented by a novel Supervised Spatial Divide-and-Conquer Network (SS-DCNet). It can learn from a closed set but generalize to open-set scenarios via S-DC. We provide mathematical analyses and a controlled experiment on synthetic data, demonstrating why closed-set modeling works well. Experiments show that SS-DCNet achieves state-of-the-art performance in crowd counting, vehicle counting and plant counting. SS-DCNet also demonstrates superior transferablity under the cross-dataset setting. Code and models are available at: https://git.io/SS-DCNet .
A Framework for Cloud Security Risk Management based on the Business Objectives of Organizations

Security is considered one of the top ranked risks of Cloud Computing (CC) due to the outsourcing of sensitive data onto a third party.In addition, the complexity of the cloud model results in a large number of heterogeneous security controls that must be consistently managed.Hence, no matter how strongly the cloud model is secured, organizations continue suffering from lack of trust on CC and remain uncertain about its security risk consequences.Traditional risk management frameworks do not consider the impact of CC security risks on the business objectives of the organizations.In this paper, we propose a novel Cloud Security Risk Management Framework (CSRMF) that helps organizations adopting CC identifies, analyze, evaluate, and mitigate security risks in their Cloud platforms.Unlike traditional risk management frameworks, CSRMF is driven by the business objectives of the organizations.It allows any organization adopting CC to be aware of cloud security risks and align their low-level management decisions according to high-level business objectives.In essence, it is designed to address impacts of cloud-specific security risks into business objectives in a given organization.Consequently, organizations are able to conduct a cost-value analysis regarding the adoption of CC technology and gain an adequate level of confidence in Cloud technology.On the other hand, Cloud Service Providers (CSP) is able to improve productivity and profitability by managing cloud-related risks.The proposed framework has been validated and evaluated through a use-case scenario.
Interoperability Techniques in CrowdHEALTH project: The Terminology Service

Introduction: Healthcare information systems' (HIS) lack of interoperability remains a challenge and a barrier for important health-related events detection.While relevant techniques are based on medical standards and technologies, these techniques do not follow a holistic approach.The creation of a set of tools that fulfils the needs of interoperability is needed.Aim: The aim of this paper is to present the terminology service envisioned while defining the initial design of the Interoperability solution proposed for the CrowdHEALTH project.Methods: In the CrowdHEALTH project, specific subcomponents responsible for providing the appropriate functionalities have been designed: The rule engine for the implementation of the business logic, the Structure Mapping Service which is responsible for creating and managing the knowledge related to the link that exists between information structures, or mappings between them and the Terminology Service for providing a set of operations on medical terminologies used for the coding of medical knowledge, which fill the information structures.Results: Therefore, it is possible to provide a series of functionalities about these information elements found within more complex structures expressed in a local code and translated into other standardized medical terminology.Towards this end, CrowdHEALTH presents the terminology service envisioned in the context of the initial design of the interoperability solution.Conclusion:CrowdHEALTH project provides an infrastructure to convert the clinical information into meaningful data so that healthcare systems communicate effectively.This initial proposal will be further extended and tested during the project life circle.
Reversible-Enhanced Stego Block Chaining Image Steganography: A Highly Efficient Data Hiding Technique

The secure exchange of secret information is one of the most important challenges in the era of modern communication. To address the issue and establish secure communication, various techniques have been developed. Transmitting secret information in a hidden manner is one of the numerous possible solutions. It can be used to transmit secret information, hidden in other media called cover medium. However, this increases the amount of transmittable data and bandwidth requirements. The work proposed technique is an effort to hide secret information in a cover medium to achieve secure communication, reduce the bandwidth requirement, and ensure the retrieval of secret information in full-strength. The proposed data hiding technique has a high data hiding capacity (HC), provides a high level of security, and results in high-quality stego images. The proposed technique, reversible-enhanced stego block chaining (RESBC), has demonstrated very good results as compared to the state-of-the-art steganography techniques. The RESBC successfully solves the data rate requirement of SBC. It has an improved security level of secret information as compared to the 4-least significant bits (4LSB) steganography. The RESBC has solved the problem of information recovery, associated with ESBC and ensures full health recovery.
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems

Inverse problems play a central role for many classical computer vision and image processing tasks. Many inverse problems are ill-posed, and hence require a prior to regularize the solution space. However, many of the existing priors, like total variation, are based on ad-hoc assumptions that have difficulties to represent the actual distribution of natural images. Thus, a key challenge in research on image processing is to find better suited priors to represent natural images. In this article, we propose the Adaptive Quantile Sparse Image (AQuaSI) prior. It is based on a quantile filter, can be used as a joint filter on guidance data, and be readily plugged into a wide range of numerical optimization algorithms. We demonstrate the efficacy of the proposed prior in joint RGB/depth upsampling, on RGB/NIR image restoration, and in a comparison with related regularization by denoising approaches.
Offline versus Online Representation Learning of Documents Using External Knowledge

An intensive recent research work investigated the combined use of hand-curated knowledge resources and corpus-driven resources to learn effective text representations. The overall learning process could be run by online revising the learning objective or by offline refining an original learned representation. The differentiated impact of each of the learning approaches on the quality of the learned representations has not been studied so far in the literature. This article focuses on the design of comparable offline vs. online knowledge-enhanced document representation learning models and the comparison of their effectiveness using a set of standard IR and NLP downstream tasks. The results of quantitative and qualitative analyses show that (1) offline vs. online learning approaches have dissimilar result trends regarding the task as well as the dataset distribution counts with regard to domain application; (2) while considering external knowledge resources is undoubtedly beneficial, the way used to express relational constraints could affect semantic inference effectiveness. The findings of this work present opportunities for the design of future representation learning models, but also for providing insights about the evaluation of such models.
Revisiting <i>L</i> <sub>2,1</sub>-Norm Robustness With Vector Outlier Regularization

In many real-world applications, data usually contain outliers. One popular approach is to use the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm function as a robust loss/error function. However, the robustness of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm function is not well understood so far. In this brief, we propose a new vector outlier regularization (VOR) framework to understand and analyze the robustness of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm function. Our VOR function defines a data point to be the outlier if it is outside a threshold with respect to a theoretical prediction, and regularizes it, i.e., pull it back to the threshold line. Thus, in the VOR function, how far an outlier lies away from its theoretical predicted value does not affect the final regularization and analysis results. One important aspect of the VOR function is that it has an equivalent continuous formulation, based on which we can prove that the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm function is the limiting case of the proposed VOR function. Based on this theoretical result, we thus provide a new and intuitive explanation for the robustness property of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm function. As an example, we use the VOR function to matrix factorization and propose a VOR principal component analysis (PCA) (VORPCA). We show some benefits of VORPCA on data reconstruction and clustering tasks.
Navigating choppy discourses: A conceptual framework for understanding synchronous text-based computer-mediated communication

Abstract This paper presents an investigation into patterns of interaction in synchronous, text-based computer-mediated communication (SCMC) with a focus on participation. The data constitute a corpus of 3,785 words from a series of interactions between student teachers performing learning activities in a three-dimensional online environment. Drawing on a systemic functional grammar perspective of language, we aim to develop a conceptual framework for understanding participation as made up of interactional patterns in students’ linguistic exchanges while performing learning activities. Our findings show that verbal negotiation that creates a shared understanding of what the group should accomplish or a common view of how to perform the activity is more frequent than strict activity organization. We argue that there are features of CMC signaling higher levels of complexity than those ordinarily found in face to face or written communication. The three broad interactional features identified – blending, turn-taking collaboration and delay endurance – corroborate our claim. This paper presents evidence that participation in text-based SCMC with learning purposes demands a disposition for collaboration and solidarity, a capacity to endure delay, and the ability to manage a conglomerate of information and communication tools without instructions on how-to processes.
Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal Databases

Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Weighted Frequent Neighborhood Itemsets (WFNI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an WFNI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated WFNIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all WFNIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.
Twitter Buzzer Detection for Indonesian Presidential Election

The campaign that was done in social media has high correlation to the supporters who disseminating the information deliberately, which called as buzzer. However, data that were generated by buzzer accounts can be considered as noise and need to be removed. In this research we performed task for detecting the buzzer accounts in Twitter by observing the impact of features we used which we selected based on their Mutual Information scores. We examined the performance of four machine learning algorithms which are Ada Boost (AB), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Histogram-based Gradient Boosting (HGB). The algorithms were evaluated using 10 folds cross validation and the results show that the best accuracy and precision achieved by AB which are 62.3% and 61.3% respectively with 25 features while the recall attained by XGB (67.9%) which the score same with its recall result with 20 features.
Agency and Body Ownership in Immersive Virtual Reality Environments: A Laboratory Study

Virtual reality (VR) technologies such as head-mounted displays are gaining increasing attention since the Oculus Rift development kit entered the market in 2016. VR is assumed to offer great potential for different purposes such as entertainment, gaming, education, or healthcare. Briefly summarized, VR provides an enclosed virtual environment in which users can become immersed, can move and look freely at 360-degree in any direction, and they can interact, manipulate, or create virtual objects with their entire body. With regard to these properties and the characteristic of immersion (a user’s state of deep involvement with an IT) we examine how immersion can be enhanced by the theoretical constructs of agency and body ownership in a VR space. Therefore, we conduct a laboratory study to enhance the perception of being in voluntary control of actions (agency) and the sense of the virtual body as one’s own one (body ownership) with 69 participants.
Performance Enhancement of Robust Cubature Kalman Filter for GNSS/INS Based on Gaussian Process Quadrature

The sigma-point Kalman filters are generally considered to outperform extended Kalman filter in the application of GNSS/INS, where cubature Kalman filter (CKF) is widely approved because of its rigorous mathematic derivation.In order to improve the robustness of GNSS/INS under GNSS-challenged environment, a robust CKF (RCKF) is developed based on novel sigma-point update framework (NSUF) in our previous work, whereas the efficiency of NSUF is still plagued by the unknown process model uncertainty.In this paper, an enhanced RCKF is proposed based on Gaussian process quadrature (GPQ), where the uncertainty consisted in sigma points transform is processed by GPQ conditioning on the approximated posterior PDF.Experiment result on loosely coupled GNSS/INS demonstrates the superiority of proposed method, where the heading error and roll error are reduced by 60.5% and 37.5% respectively compared with RCKF, and it achieves better position result than GP-CKF under GNSS outage.
Fusing Self-Organized Neural Network and Keypoint Clustering for Localized Real-Time Background Subtraction

Moving object detection in video streams plays a key role in many computer vision applications. In particular, separation between background and foreground items represents a main prerequisite to carry out more complex tasks, such as object classification, vehicle tracking, and person re-identification. Despite the progress made in recent years, a main challenge of moving object detection still regards the management of dynamic aspects, including bootstrapping and illumination changes. In addition, the recent widespread of Pan–Tilt–Zoom (PTZ) cameras has made the management of these aspects even more complex in terms of performance due to their mixed movements (i.e. pan, tilt, and zoom). In this paper, a combined keypoint clustering and neural background subtraction method, based on Self-Organized Neural Network (SONN), for real-time moving object detection in video sequences acquired by PTZ cameras is proposed. Initially, the method performs a spatio-temporal tracking of the sets of moving keypoints to recognize the foreground areas and to establish the background. Then, it adopts a neural background subtraction, localized in these areas, to accomplish a foreground detection able to manage bootstrapping and gradual illumination changes. Experimental results on three well-known public datasets, and comparisons with different key works of the current literature, show the efficiency of the proposed method in terms of modeling and background subtraction.
OperativeCriticalPointBug algorithm-local path planning of mobile robot avoiding obstacles

&lt;span&gt;For Autonomous Mobile Robot one of the biggest and interesting issues is path planning. An autonomous mobile robot should be able determine its own path to reach destination. This paper offers a new algorithm for mobile robot to plan a path in local environments with stationary as well as moving obstacles. For movable robots’ path planning OperativeCriticalPointBug (OCPB) algorithm, is a new Bug algorithm. This algorithm is carried out by the robot throughout the movement from source to goal, hence allowing the robot to rectify its way if a new obstacle comes into the route or any existing obstacle changes its route. According as, not only the robot tries to avoid clash with other obstacle but also tries a series of run time adjustment in its way to produce roughly a best possible path. During journey the robot is believed to be capable to act in an unknown location by acquiring information perceived locally. Using this algorithm the robot can avoid obstacle by considering its own as well as the obstacle’s dimension. The obstacle may be static or dynamic. The algorithm belongs to bug family.&lt;/span&gt;
Understanding the Role of Incentives in Security Behavior

A key challenge for researchers has been to affect change in user security behavior in organizations.Several theories from different domains have been used for understanding and changing user security behavior including deterrence, fear appeals, and education; however, the success of these approaches has been low.In this research we examine the role of financial incentives in changing user behavior; we specifically provide incentives to users for good security practices.The study attempts to switch user behavior such that they adopt good security habits however it recognizes the limitations of extrinsic rewards in being temporary and couples the extrinsic rewards to affect intrinsic motivation through use of nudges.The field study shows positive results however the number of subjects in our study was small (24).Our goal is to extend the study by large scale data collection to further validate our results.
Medium term electricity price forecasting using extreme learning machine

Accurate electricity market price forecasting gives a capability to make better decisions in electricity market environment when, this market is complicated due to the severe fluctuations. The main purpose of a prediction model is forecasting the future prices. For doing this, the predicted variable (as output) and historical data (as input) should be close to each other. Machine learning is known as one of the most successful way of forecasting time series. Extreme learning machine (ELM) is a feed- forward neural network with one hidden layer. Hence, in this paper an extreme learning machine has been used for predicting electricity prices in a medium term time horizon. The real data of New York City electricity market has been utilized to simulate and predict the electricity price in four seasons of the year. Finally, the findings will be compared with multi-layer perceptron (MLP) results, which proves the efficiency of the model.