Reviewerly/exHarmony huggingface dataset:
On the Photon Subtraction-Based Measurement-Device-Independent CV-QKD Protocols

To potentially overcome the practical security loopholes of CV-QKD protocols, in this paper, we propose to use the optimized eight-state measurement-device-independent (MDI) protocol and demonstrate that it can significantly outperform corresponding Gaussian modulation-based MDI and virtual photon subtraction-based MDI CV-QKD protocols in terms of both secret-key rate and achievable transmission distance. Contrary to the common belief that virtual photon subtraction method can extend the distance of MDI CV-QKD protocols, we show that this is not true for fully optimized MDI CV-QKD protocols and realistic system parameters.
A Fast Aerial Images Mosaic Method Based on ORB Feature and Homography Matrix

Image mosaic is of great significance for the research of computer vision. However, the environment is constantly changing over time. Accordingly, its image mosaic also needs to be regularly updated to support various applications, including comprehensive improvement of road environment, resolution enhancement and motion detection. A fundamental step for panoramic image is to register aerial images taken from different view efficiently and effectively. Aiming at the problem of low speed and low accuracy for extracting features from aerial images. An improved method based on ORB feature for image mosaic is proposed in this paper. This image mosaic process consists of four steps: Firstly, we analyze the performance and speed of the existing several popular feature extraction algorithms and propose to use ORB feature because of its more effective calculation speed. Secondly, we introduce a fast and robust feature matching strategy based on descriptors similarity by setting an appropriate threshold. Thirdly, After the matching feature set is determined, the transformation model is constructed and the model parameters are obtained, the progressive LMedS&LS algorithm is applied to eliminate false matches. Finally, the multi-band fusion algorithm is used to fuse matched images and realize panoramic mosaic. A large number of experiments demonstrated this method is efficient in the stages of feature extraction of images stitching and matching.
Fuzzy Deep Stack of Autoencoders for Dealing with Data Uncertainty

This paper addresses the problem of dealing with uncertainty on neural networks for the specific case of Autoencoders. The recently introduced concepts of `Autoencoders' and `Deep Stacks of Autoencoders' (DSAE) and their use for dimensionality reduction and data compression problems have gained considerable attention and reached very promising results. However, similarly to the traditional neural networks, Autoencoders are deterministic structures that are not very suitable for dealing with data uncertainty, a very important aspect of the real-world applications. In this paper, we propose a fuzzy approach to reduce uncertainty on stacks of Autoencoders by automatically adding qualitative fuzzy information about the data to the input layer. This can be accomplished by adding a fuzzy layer-0 to the stack of Autoencoders that extracts fuzzy knowledge from the crisp data set and includes that knowledge as extra information to the network input. The approach is completely transparent to the network and to the user and, theoretically, can be generalized to any architecture of neural network, including convolutional neural networks. The results presented here are very encouraging and present substantial improvement, especially when dealing with noisy data.
Evaluation of augmented reality SDKs for classroom teaching

Augmented Reality (AR) has been used in different application domains. In education, it usually aims to improve the current teaching methodologies and is known as Augmented Learning. Currently, there are several Software Development Kits (SDKs), that requires a qualitative analysis to determine which of these tools are appropriate for use as academic support in face-to-face teaching. This paper evaluates the main SDKs for the development of a marker-based AR software to use in classrooms based on a case study in a public university in Brazil. Our study evaluated the accuracy of detecting markers with different characteristics in each of the SDKs. A comparative analysis is presented. The conclusion is that Vuforia and EasyAR are the better choices among the other selected SDKs for this evaluation.
Impact of Organizational Ambidexterity on Organizational Conflict of Zain Telecommunication Company in Jordan

Objectives: The present research was conducted to identify the impact of Organizational Ambidexterity on Organizational Conflict. Methods/Statistical analysis: The study population consisted of all of the managers of Zain Telecommunication Company in Jordan. The questionnaire comprised three parts covered the intended constructs, i.e., Organizational Ambidexterity on Organizational Conflict. The questionnaires, with instructions of how to complete them, were used, (260) questionnaires retrieved, to analyze hypothesis data, Smart Equal Partial Least Square and Structure Equation Modeling (PLS-SEM) were used as an approach for analysis. Findings: The results of the study indicate that Organizational Ambidexterity had a significant effect on organizational conflict. Exploration has the highest impact on organizational conflict. Application/Improvements: Organization with exploitation constantly responds to environmental changes and strives to meet client requirements by working continuously on teaching knowledge, experience and new skills for employees. Keywords: Jordan, Organizational Ambidexterity, Organizational Conflict, Telecommunication Company
Sepsis Prediction: An Attention-Based Interpretable Approach

Sepsis is the leading cause of death in ICUs and a very costly medical phenomena. The earlier it is predicted, the less inpatient mortality and the less the length of ICU stay, thus a major cut in medical expenses. Although the current deep learning models are able to make predictions about the possibility of sepsis in the ICU, they still lack the ability to reveal the major factors that lead to the outcomes of the predictions. In this paper, we have explored the use of an attention-based model in prediction of sepsis which provides more details on the amount of contribution of each of the medical measurements to the final prediction. This would help health care providers to improve their procedures to reduce sepsis related mortality rate.
Additive Quaternary Codes Related to Exceptional Linear Quaternary Codes

We study additive quaternary codes whose parameters are close to those of the extended cyclic [12, 6, 6]4-code or to the quaternary linear codes generated by the elliptic quadric in PG(3, 4) or its dual. In particular we characterize those codes in the category of additive codes and construct some additive codes whose parameters are better than those of any linear quaternary code. Our new code parameters are [22, 17.5, 4]4.
Modeling observability in adaptive systems to defend against advanced persistent threats

Advanced persistent threats (APTs) are a particularly troubling challenge for software systems. The adversarial nature of the security domain, and APTs in particular, poses unresolved challenges to the design of self-* systems, such as how to defend against multiple types of attackers with different goals and capabilities. In this interaction, the observability of each side is an important and under-investigated issue in the self-* domain. We propose a model of APT defense that elevates observability as a first-class concern. We evaluate this model by showing how an informed approach that uses observability improves the defender's utility compared to a uniform random strategy, can enable robust planning through sensitivity analysis, and can inform observability-related architectural design decisions.
Logical Support for Bike-Sharing System Design

Automated bicycle-sharing systems (bss) are a prominent example of reconfigurable cyber-physical systems for which the locality and connectivity of their elements are central to the way in which they operate. These features motivate us to study bss from the perspective of Actor-Network Theory – a framework for modelling cyber-physical-system protocols in which systems are networks of actors that are no longer limited to programs but can also include humans and physical artefacts. In order to support logical reasoning about information-flow properties that occur in bss, we use a logical framework that we have recently developed for actor networks, which results from a two-stage hybridization process. The first stage corresponds to a logic that captures the locality and connectivity of actors in a given configuration of the network; the second stage corresponds to a logic of possible interactions between actors, which captures the dynamics of the system in terms of network reconfigurations. To illustrate the properties that can be checked using this framework, we provide an actor-network specification of a particular bss, and use a recently developed tool for hybridized logics to highlight and correct an information-flow vulnerability of the system.
Machine Learning Based Frame Classification for Videos Transmitted over Mobile Networks

In wireless systems, content-aware MAC layer scheduling strategies contribute to supporting an adequate Quality of Service (QoS) and Quality of Experience (QoE) for the users. Content related information that can be used in such strategies include information about the video frame type (Intra-Predicted, Inter-Predicted or Backward-Predicted). Frame type identification and prediction in the MAC layer scheduler (as well as in other network locations) is hence a critical part of content-aware resource management and traffic engineering in video streaming. It is important to note that, when encryption is adopted at the upper layers, packet inspection is not possible. To address this issue, we propose an adaptive clustering and prediction algorithm. The algorithm uses unsupervised clustering combined with an adaptive classification approach to automatically group packets into frame clusters. Unlike conventional video traffic classifiers, our approach requires neither a-priori knowledge of video frames pattern nor a training set of frames. Instead, this approach automatically classifies packets into different frame types by employing simple statistical features. The proposed method continuously learns from the upcoming traffic thus the proper frame class labels can be easily discovered. Furthermore, the proposed method uses an autoregressive integrated moving average (ARIMA) algorithm to predict the upcoming traffic frame type. A large number of experiments has been carried out using data from several video samples. Results show the robustness and the effectiveness of our classification method, which is capable of achieving a detection rate of up to 97.3% for I frames and overall 2 identification accuracy of 91.3% (for all I, P and B frames).
Datafication, value and power in developing countries: Big data in two Indian public service organizations

Abstract Motivation Datafication—the growing presence, use and impact of data in social processes—is spreading to all sectors in developing countries. But, to date, there are few analyses of real‐world experiences of datafication in developing country organizations. Purpose We address this knowledge gap by analysing evidence of big data in practice in relation to three key issues: implementation, value and power. Approach and methods Using interview, observation and documentary sources, we analyse the implementation and impact of big data systems in Indian electricity and transport public sector organizations. Findings Big data systems have been much slower to implement than anticipated, and the article exposes the nature and scale of the implementation challenge facing such systems. These are already delivering value for some managers within public service organizations but are, as yet, more operational than strategic and incremental not transformative. Big data systems are facilitating a shift in power from the public sector to the private sector, and from labour and middle management to panopticon‐type control by central managers. Big data intersects with politics especially around the imaginaries of wider stakeholders, changing their view of the financial and political issues that technology can address. Policy implications Policy‐makers and practitioners can better understand and plan for big data in development using three frameworks presented in the article: information value chain, decision pyramid, and big data–power model. These expose key issues of implementation, organizational value and power that must be incorporated into big data policy and projects. Benefits of datafication have been largely restricted to senior managers, private contractors and some politicians. To spread these to other stakeholders, including workers and citizens, action must be taken to address both practical and political issues arising in the datafication of development.
Unbalanced Multistage Heat Conduction and Mass Diffusion Algorithm in an Educational Digital Library

Discovering valuable and diverse resources in an educational digital library can be difficult with the existing large number of content collections. Researchers in information retrieval and related domains have moved from considering only keyword-based matching to modeling the underlying behavior pattern of users. To achieve the purpose of both precision and diversity while providing online educational resource services, in this paper, we propose a weighted network-based information filtering framework that models user usage as a bipartite user-resource network; users and resources are treated as nodes in this network, each edge from a user to a resource means usage, and the weight represents the accumulation of multiple usage scenarios. Under this framework, we propose two individual algorithms, the unbalanced heat conduction algorithm and the unbalanced mass diffusion algorithm, and one hybrid multistage heat conduction and mass diffusion algorithm. There are two stages in these algorithms. In stage 1, an initial energy is assigned to each resource that has been visited by the target and passes to users according to a specific strategy. In stage 2, the energy is similarly transferred from users to resources, and a sorted resource list ranked by energy is presented to the target user. Experiments on a real-world dataset of one year of academic search logs showed improved performance from multiple indicators compared to existing algorithms.
Scope and definition of user experience in Brazil

For over a decade, the term User Experience (UX) has been widely used in both industry and academia. Still, there is no consensus on several central aspects of UX, such as the exact definition of the term, what it encompasses, how to evaluate it, among other elements. The different understandings bring practical and theoretical implications that impact the development of interactive systems in industry and the research of related areas in academia. This fact has motivated the conduction of several international studies with the purpose to establish the scope and definition of UX, as well as to understand the nuances of the area, mapping how it is understood in different contexts. Thus, to investigate how the Brazilian research and practice community understands UX, this paper replicates the research initially conducted in 2009 and later replicated in 2015. Both studies used a survey to identify how researchers and practitioners from different nationalities and backgrounds understand and define UX. In the present work, we obtained 216 responses from Brazilian students, researchers and professionals working in the area. The results of the analysis indicate similarities between the national and international communities and highlight significant differences regarding the understanding of this concept in Brazil.
Dealing Big Data using Fuzzy C-Means (FCM) Clustering and Optimizing with Gravitational Search Algorithm (GSA)

Any data in the real world can be organized properly by Clustering the data using some clustering techniques and in these clustering techniques Fuzzy C-Means (FCM) is a very recent and better technique that can mold the data with good logic and in a highly accurate manner. FCM is same as K-Means clustering technique but FCM is developed with some Fuzzy means. FCM is associated with some constraints as FCM is more responsive as on the order how the clusters were arranged in the beginning of applying the technique. Even though it has its own defects when dealing with large data, it cannot get the finest solution as the data need some optimization? So to get some better accuracy, In our proposed paper we are applying an enhanced FCM for clustering and for obtaining precise results we are involving GSA based on gravitation laws and perception of masses. This optimizing technique (GSA) can be used to improve the limitations in order to gain a well-defined system performance; GSA can efficiently deal with many propositions of large data. Here we are going to develop a Map-Reduce mechanism to effectively deal with large data. The expected result can be achieved by choosing the better finest candidates and grouping them in the reduction mode to get the finest solution. Evaluation of practical results will be optimizing when compared to other existing similar techniques.
Towards an Integrated Hybrid Mobile Application for Smart Campus Using Location-Based Smart Notification

Smart city is a popular concept in recent years, the move towards a smart city has already been started by several cities around the world. Smart campus is a vital component that could shape a smart city. It is a more specific field that focuses the technology development in a university or campus environment. Along with the rapid growth in the research and practical field regarding smart campuses, mobile technology is another field that is also growing rapidly, while the field is not a new one it is still relevant to these days and still continues to be developed and improved. Mobile devices are widely used in a campus environment, thus could play a huge role in a smart campus. As a first step towards an integrated mobile app for smart campuses, this paper shows our early result in a form of a meeting room booking management application which utilizes an indoor location-based service using Wi-Fi fingerprints to implement smart notification feature into the app to minimize or even eliminate the possibility of interruption overload that are often experienced by users of mobile application caused by the sheer amount of notifications. The application and the smart notification feature are then evaluated by looking at the users' perceived usefulness and ease of use, and the result shows that it has potential to be further developed and improved to be a mobile hub towards a smart campus.
Notes on the Dynamics of Hyperbolic Tangent Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCMs) are recurrent neural networks applied for modelling complex systems and structures. In this method, the system is represented by a weighted, directed digraph, where the nodes of the network represent the main characteristics of the modelled system, while the weighted and directed edges correspond to the direction and strength of causal relationships between these factors. The FCM based decision making is based on the so-called activation values of the nodes, which represents the state of the system. These activation values are determined by an iteration, which may lead to an equilibrium point (fixed point), but limit cycles or chaotic behaviour may also occur. In this paper, the dynamics of fuzzy cognitive maps with hyperbolic tangent threshold function is mathematically discussed. Theoretical conditions are provided for the existence and uniqueness of fixed points. Moreover, the stability of fixed points and their basins of attractions are also analysed. The results presented here give insight into the special symmetric nature of hyperbolic FCMs.
Distill-Net

Many Internet-of-Things (IoT) applications demand fast and accurate understanding of a few key events in their surrounding environment. Deep Convolutional Neural Networks (CNNs) have emerged as an effective approach to understand speech, images, and similar high-dimensional data types. Algorithmic performance of modern CNNs, however, fundamentally relies on learning class-agnostic hierarchical features that only exist in comprehensive training datasets with many classes. As a result, fast inference using CNNs trained on such datasets is prohibitive for most resource-constrained IoT platforms. To bridge this gap, we present a principled and practical methodology for distilling a complex modern CNN that is trained to effectively recognize many different classes of input data into an application-dependent essential core that not only recognizes the few classes of interest to the application accurately but also runs efficiently on platforms with limited resources. Experimental results confirm that our approach strikes a favorable balance between classification accuracy (application constraint), inference efficiency (platform constraint), and productive development of new applications (business constraint).
Comparison of Various Fault Tolerance Techniques for Scientific Workflows in Cloud Computing

Cloud computing has become a developing platform for personal or individual computing. For enhancing capabilities of cloud, fault tolerance is a very essential task. Fault tolerance is one of the major issue among all the issues of cloud computing. The principle advantages of having fault tolerance in cloud computing incorporate lower cost, failure recovery and enhanced performance metrics. In this paper, we have reviewed various techniques for optimising the fault tolerance in scientific workflows. The fault tolerance strategy reduces the cost, scheduling overhead and scalability etc. It is impossible to satisfy all the requirements for effective fault tolerance as it violates the service level agreement.
Finite Horizon Maximum Likelihood Estimation for Integrated Navigation with RF Beacon Measurements

Abstract We develop a Finite Horizon Maximum Likelihood Estimator (FHMLE) that fuses Inertial Measurement Unit (IMU) and radio frequency (RF) measurements over a sliding window of finite length for three‐dimensional navigation. Available RF data includes pseudo–ranges, angles of transmission (AoT), and Doppler shift measurements. The navigation estimates are obtained by solving a finite‐dimensional nonlinear optimization using a primal‐dual interior point algorithm (PDIP). The benefits of the proposed estimation method are highlighted using simulations results comparing the FHMLE approach with an Unscented Kalman Filter (UKF), in a scenario where an aircraft approaches a carrier, with RF measurements from beacons aboard the carrier, and low‐cost IMU measurements aboard the aircraft. When the Geometric Dilution of Precision is large, we found that the FHMLE is able to achieve smaller estimation errors than the UKF, which tends to carry a bias throughout the trajectory.