Reviewerly/exHarmony huggingface dataset:
Wavelet transform based modulation classification for 5G and UAV communication in multipath fading channel

Nowadays, fifth generation (5G) network and unmanned aerial vehicle (UAV) are more and more important in the civil and military field. Only communicating correctly in 5G network and between UAVs, they can play a role in real world. Modulation classification is the premise to ensure communication in 5G network and between UAVs correctly. However, the effects of multipath fading always exists in the 5G communication environment and UAV communication channel, which leads to severe modulation classification performance and communication performance degradation. In order to resolve this problem, we proposed a novel modulation classification algorithm that can classify signals without priori information in multipath channel. The proposed algorithm makes the mean, variance, skewness and kurtosis of wavelet transform as the feature set, then uses principal component analysis (PCA) for feature subset selection, in the end neural network is used as classifier to classify signals. The simulation results show that the proposed algorithm can achieve the much better classification accuracy than the existing methods in multipath fading channels.
An empirically evaluated checklist for surveys in software engineering

Context: Over the past decade Software Engineering research has seen a steady increase in survey-based studies, and there are several guidelines providing support for those willing to carry out surveys. The need for auditing survey research has been raised in the literature. Checklists have been used both to conduct and to assess different types of empirical studies, such as experiments and case studies. Objective: To operationalize the assessment of survey studies by means of a checklist. To fulfill such goal, we aim to derive a checklist from standards for survey research and further evaluate the appropriateness of the checklist in the context of software engineering research. Method: We systematically aggregated knowledge from 12 methodological studies supporting survey-based research in software engineering. We identified the key stages of the survey process and its recommended practices through thematic analysis and vote counting. We evaluated the checklist by applying it to existing surveys and analyzed the results. Thereafter, we gathered the feedback of experts (the surveys’ authors) on our analysis and used the feedback to improve the survey checklist. Results: The evaluation provided insights regarding limitations of the checklist in relation to its understanding and objectivity. In particular, 19 of the 38 checklist items were improved according to the feedback received from experts. Conclusion: The proposed checklist is appropriate for auditing survey reports as well as a support tool to guide ongoing research with regard to the survey design process. A discussion on how to use the checklist and what its implications are for research practice is also provided.
Matrix Coding-Based Quantum Image Steganography Algorithm

Embedding secret information into quantum carrier image for covert communication is one of significant research fields of quantum secure communication. Using good imperceptibility and high embedding efficiency of matrix coding, this paper proposes a novel matrix coding-based quantum steganography algorithm for quantum color images. In order to better apply matrix coding in actual demand, two different embedding methods are proposed. One embedding method is single pixel-embedded (1, 3, 2) coding, called SPE (1, 3, 2) coding for short. This method embeds two qubits of secret information into three least significant qubits (LSQbs) of a single pixel of quantum carrier image, and at most only one LSQb would be changed. The other embedding method is the multiple pixels-embedded (1, 3, 2) coding, called MPsE (1, 3, 2) coding, in which three LSQbs of multiple carrier pixels are utilized to embed two secret qubits. On account of experimental simulation obtained in the MATLAB environment, it shows that the new algorithm has good performance in the imperceptibility, the security, the embedding efficiency, and the embedding capacity.
News Media's Rhetoric on Facebook

While Facebook is an important distribution channel for today's media houses, there is a lack of research on how news outlets choose to present their stories in social media. The present study aims to narrow this gap by analysing two weeks of Facebook updates by the Norwegian tabloid Dagbladet and the public-service broadcaster NRK and comparing them to the corresponding stories on their news sites. An important objective is to uncover if and how the Facebook updates depart from established text norms for online papers. The method is triangulated. A quantitative content analysis reveals that newsrooms tend to utilize a wider range of speech acts when writing presentations specifically for Facebook. A follow-up qualitative analysis identifies five rhetorical strategies for unique promo texts on Facebook: adding emojis, posing questions, making requests, expressing emotions and stating subjective points of view. Qualitative interviews with responsible journalists confirm that these strategies are more common the less controversial the stories are. However, the newsrooms have few explicit guidelines for when it is acceptable to transgress traditional journalistic text norms. The findings are summarized in a model that connects the continuum of decreasing story controversy to a corresponding continuum of increasingly interpretative and subjective rhetoric.
The Digital Knowledge Economy Index: Mapping Content Production

We propose the construction of a Digital Knowledge Economy Index, quantified by way of measuring content creation and participation through digital platforms, namely the code sharing platform GitHub, the crowdsourced encyclopaedia Wikipedia, and Internet domain registrations and estimating a fifth sub-index for the World Bank Knowledge Economy Index for year 2012. This approach complements conventional data sources such as national statistics and expert surveys and helps reflect the underlying digital content creation, capacities, and skills of the population. An index that combines traditional and novel data sources can provide a more revealing view of the status of the world’s digital knowledge economy and highlight where the (un)availability of digital resources may actually reinforce inequalities in the age of data.
Fast and accurate computation of Racah moment invariants for image classification

In this paper, a new set of moment invariants, named Racah Moment Invariants (RMI), is introduced in the field of image analysis. This new set can be used to describe pattern feature independently of Rotation, Scaling and Translation transforms. Moreover, new fast and accurate algorithm, using recursive method, is developed for accelerating the computation time of the newly proposed invariants, as well as, for enhancing their numerical stability. Subsequently, several experiments have been performed. Initially, the numerical stability and computational cost are depicted. Secondly, the global and local features extraction are clearly illustrated. Then, invariability property and noise robustness are investigated. Finally, the discrimination power and the classification accuracy of the proposed invariants are extensively tested on several publicly available databases. The presented theoretical and experimental results, clearly show that the proposed method can be extremely useful in the fields of image classification.
Cloud storage auditing with deduplication supporting different security levels according to data popularity

The public cloud storage auditing with deduplication is proposed to check the integrity of cloud data under the condition that the cloud stores only a single copy of the same file from different users. To the best of our knowledge, the existing schemes about cloud storage auditing with deduplication cannot support semantic security for cloud data. The recent data breach incidents have led to an increased demand for the security of encryption schemes. Under the circumstances, we consider to provide different security levels according to the popularity of cloud data. We use the semantic secure encryption to encrypt the unpopular data (private data) to realize semantic security and use convergent encryption to encrypt popular data to realize ciphertext deduplication. However, there exists a big challenge for cloud storage auditing when data popularity changes. Because encryption algorithms are different for popular data and unpopular data, the corresponding ciphertext will have to change once data popularity changes. The old authenticators cannot be valid for the integrity checking any longer after ciphertext changes. In order to overcome this challenge, we explore the numerical relationship between old authenticators and new ones. In our designed scheme, it is not necessary for users to be online for doing extra computation when data popularity changes. The cloud can perform the task of authentications transforming to ensure that the cloud storage auditing still smoothly runs. By detailed security Proof and performance analysis, we show that the proposed scheme is secure and efficient.
Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the motion of galaxies and stars in the universe. This algorithm gives us the possibility of finding the global optimum with greater precision since it uses multiple exploration and exploitation cycles. In this paper we present a modification to galactic swarm optimization using type-1 (T1) and interval type-2 (IT2) fuzzy systems for the dynamic adjustment of the c3 and c4 parameters in the algorithm. In addition, the modification is used for the optimization of the fuzzy controller of an autonomous mobile robot. First, the galactic swarm optimization is tested for fuzzy controller optimization. Second, the GSO algorithm with the dynamic adjustment of parameters using T1 fuzzy systems is used for the optimization of the fuzzy controller of an autonomous mobile robot. Finally, the GSO algorithm with the dynamic adjustment of parameters using the IT2 fuzzy systems is applied to the optimization of the fuzzy controller. In the proposed approaches, perturbation (noise) was added to the plant in order to find out if our approach behaves well under perturbation to the autonomous mobile robot plant; additionally, we consider our ability to compare the results obtained with the approaches when no perturbation is considered.
Controlled teleportation of an arbitrary two-qubit entanglement in noises environment

We present a scheme for implementing tripartite controlled teleportation of an arbitrary two-qubit entanglement state with the genuine pentaqubit entangled state and generate the quantum channel and then demonstrated the feasibility of probabilistic controlled teleportation in cavity QED. We also used fidelity and concurrence to quantify the efficiency of the protocol in noisy environment. Several noise scenarios are investigated in which the bit of the controller and the receiver are subjected to the same or different types of noise. The result has shown that in some specially noise scenario, lower fidelity means higher entanglement for output state and it also exhibits more noise which leads to more efficiency for entanglement.
Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems

With the rapid growth of public cloud offerings, how to design effective prediction models that provide appropriate recommendations for potential users has become more and more important. In dynamic cloud environment, both of user behaviors and service performance are sensitive to contextual information, such as geographic location information. In addition, the increasing number of attacks and security threats also brought the problem that how to protect critical information assets such as sensitive data, cloud resources and communication in a more effective and secure manner. In view of these challenges, we propose a privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems. Specifically, our method is designed as a three-phase process: Firstly, two privacy-preserving mechanisms, i.e., a randomized data obfuscation technique and a region aggregation strategy are presented to protect the private information of users and deal with the data sparsity problem. Then a location-aware latent factor model based on tensor factorization is applied to explore the spatial similarity relationships between services. Finally, predictions are made based on both global and spatial nearest neighbors. Experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method achieves decent prediction accuracy on the premise of privacy preservation.
Distributed learning from multiple EHR databases: Contextual embedding models for medical events

Electronic health record (EHR) data provide promising opportunities to explore personalized treatment regimes and to make clinical predictions. Compared with regular clinical data, EHR data are known for their irregularity and complexity. In addition, analyzing EHR data involves privacy issues and sharing such data is often infeasible among multiple research sites due to regulatory and other hurdles. A recently published work uses contextual embedding models and successfully builds one predictive model for more than seventy common diagnoses. Despite of the high predictive power, the model cannot be generalized to other institutions without sharing data. In this work, a novel method is proposed to learn from multiple databases and build predictive models based on Distributed Noise Contrastive Estimation (Distributed NCE). We use differential privacy to safeguard the intermediary information sharing. The numerical study with a real dataset demonstrates that the proposed method not only can build predictive models in a distributed manner with privacy protection, but also preserve model structure well and achieve comparable prediction accuracy. The proposed methods have been implemented as a stand-alone Python library and the implementation is available on Github (https://github.com/ziyili20/DistributedLearningPredictor) with installation instructions and use-cases.
IoT based Smart Traffic density Control using Image Processing

With the rapid development of road infrastructure, the volume of vehicle on the road network increases which leads to traffic Congestion. The same scenario exists in the Sultanate of Oman. Traffic congestions are amongst the top list of the problems faced in Muscat and other cities in the Sultanate of Oman. This is mainly caused due to the rapid up rise in the number of vehicles in a short span of time. To overcome such impact of traffic congestions, it is required to develop an IoT Based traffic control system. The proposed system would be based on the measurement of the actual traffic density on the road. This would be achieved using a real time video and image processing techniques. Wherein the images captured and are stored in the server, which will be compared with the real time image captured via camera to identify the density. The theme is to control the traffic by determining the traffic density on each side of the road and enabling a controlling option of the traffic signal to the user through a software application.
A Hybrid Approach Toward Research Paper Recommendation Using Centrality Measures and Author Ranking

The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors’ collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.
Measuring the Gender and Ethnicity Bias in Deep Models for Face Recognition

We explore the importance of gender and ethnic attributes in the decision-making of face recognition technologies. Our work is in part motivated by the new European regulation for personal data protection, which forces data controllers to avoid discriminative hazards while managing sensitive data like biometric data. The experiments in this paper are aimed to study what extent sensitive data like gender or ethnic origin attributes are present in the most common face recognition networks. For this, our experiments include two popular pre-trained networks: VGGFace and Resnet50. Both pre-trained models are able to classify gender and ethnicity easily (over 95% of performance) even suppressing 80% of the neurons in their embedding layers. The experimentation is conducted on a publicly available database known as Labeled Faces in the Wild with more than 13000 images of faces with a huge range of poses, ages, races and nationalities.
Crypto-friendliness: understanding blockchain public policy

Purpose The purpose of this paper is to conceptualise the chief aspects of policy interest in blockchain technology. Design/methodology/approach The paper outlines policymaking processes in the context of innovation and technological change, assesses generic variations in policy treatment towards blockchain, and identifies manifestations of policy entrepreneurship using national case studies of blockchain policies. Findings Favourable policy dispositions towards blockchain technology are interpreted as political efforts to develop local, blockchain-enabled economies. So-called “crypto-friendly” jurisdictions proactively clarify regulatory and tax treatments of cryptocurrency and other blockchain applications, and trial blockchain uses in fields predominated by public sector activity. Policymakers in countries hostile towards blockchain-related activity have instigated bans or strict limitations with respect to blockchain engagement by developers and users. Research limitations/implications Reliance upon case studies suggests the need for alternative study approaches (e.g. index construction, empirical research) as blockchain use consolidates throughout the global economy. Practical implications This paper provides insight to policymakers and blockchain practitioners regarding the attributes of accommodative policies towards distributed ledger technology. Social implications Countries and sub-national regions exhibiting a more welcoming policy stance are more likely to attract entrepreneurs and investors in the crypto-economic blockchain space. Originality/value This paper develops a policy “crypto-friendliness” construct to assess the extent to which policymakers enact accommodative policies for blockchain development.
Survey of Non-Image-Based Approaches for Counting People

Accurate people count estimation, potentially in real-time, both for indoor and outdoor environments, is said to be of major importance in the smart cities of tomorrow. Application areas, such as public transportation, urban analytics, building automation, as well as disaster management are all expected to benefit if they were to have a better understanding of occupancy in public premises. A large body of work has been concentrated into providing people counting solutions based on images captured by surveillance cameras. However, image-based approaches are costly, as they require devoted hardware installations, and are often privacy intruding. Thus, academic and industry researchers are looking into alternative solutions for people counting. In this paper, we present a comprehensive study of non-image-based people counting techniques. Our goal with this paper is twofold: 1) to serve as an introduction to everyone interested in gaining a better understanding on non-image-based people counting techniques and 2) to serve as a guideline to practitioners interested in implementing and testing specific solutions in their everyday practice. To this end, we provide a novel classification of available approaches, and outline the requirements they need to meet. We further discuss in detail different academic solutions, and provide comparisons between them. Furthermore, we provide a discussion on available industrial approaches and compare them to academic proposals. Finally, we discuss open challenges and future directions in the field of non-image-based people counting.
Improved Biogeography-Based Optimization Algorithm and Its Application to Clustering Optimization and Medical Image Segmentation

In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, an improved BBO algorithm, that is, worst opposition learning and random-scaled differential mutation BBO (WRBBO), is presented in this paper.First, BBO's mutation operator is deleted to reduce the computational complexity and a more efficient random-scaled differential mutation operator is merged into BBO's migration operator to obtain global search ability.Second, in order to balance exploration and exploitation, the BBO's migration operator is replaced with a dynamic heuristic crossover to enhance the local search ability.Finally, a worst opposition learning is merged into the improved algorithm to avoid trapping into local optima.A large number of experiments are made on 18 various kinds of classic benchmark functions and some complex functions from the CEC-2013 test set.In addition, WRBBO is applied to clustering optimization and medical image segmentation.The experimental results show that WRBBO has better optimization efficiency on benchmark function optimization, clustering optimization, and medical image segmentation than quite a few state-of-the-art BBO variants and other algorithms.