Reviewerly/exHarmony huggingface dataset:
The Nexus Between Population and Economic Growth In Ethiopia: An Empirical Inquiry

The link between population growth and economic growth has been becoming of most significant interest for researchers.However, there is no consensus among economists and researchers about the interaction between population and economic growth.Using timeseries data spanning from the period 1981 to 2018, this study examined the populationeconomic growth nexus in the second most populous African nation-Ethiopia.Design/methodology/approach: The study used yearly time series data spanning from 1981 to 2018, Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests, Autoregressive Distributed Lag (ARDL) co-integration approach and Toda-Yamamoto Causality tests. Finding:Population and economic growth (Proxied by GDP) have a long-run association, as confirmed by the bound test co-integration approach.From ARDL model estimates, population growth is found to have a negative and significant effect on economic growth, both in the short-run and in the long-run.Likewise, growth in GDP affects population growth positively and significantly-both in the short-run and long-run.The Toda-Yamamoto Granger-Causality test revealed that, there is a unidirectional causality running from population to economic growth. Research implications:The current (as of 2019) number of the total population of Ethiopia exceeds 110 millionmaking the country the second-most populous nation in Africa.With the prevalence of high fertility rate and mortality rate, unemployment, and poverty, the population is not expected to bring economic development in general and economic growth in particular.Hence, it is advised to the concerned body that anti-natal policies that discourage the fertility rate, need to be re-considered to supplement with economic growth policies. Originality/value:This paper provides an empirical study of population-economic growth nexus in Ethiopiaa low-income country with a rapidly growing economy but also a rapidly increasing population and labor force.An understanding of such an issue would provide a reliable input for formulating development policies.However, up to the best of my knowledge, there is limited empirical research about population-growth nexus growth for the case of Ethiopia.
A Unified Non-Negative Matrix Factorization Framework for Semi Supervised Learning on Graphs

We propose a Semi-Supervised Learning (SSL) methodology that explicitly encodes different necessary priors to learn efficient representations for nodes in a network.The key to our framework is a semi-supervised cluster invariance constraint that explicitly groups nodes of similar labels together.We show that explicitly encoding this constraint allows one to learn meaningful node representations from both qualitative (visual) and quantitative standpoints.Specifically, our methodology realizes improved node classification and visually-enhanced clusterability of nodes on a wide range of datasets over competitive baselines.
Thermally pumped on-chip maser

We present a theoretical model of an on-chip three-level maser in a superconducting circuit based on a single artificial atom and pumped by a temperature gradient between thermal baths coupled to different interlevel transitions. We show that maser powers of the order of a few femtowatts, well exceeding the resolution of the sensitive bolometry, can be achieved with typical circuit parameters. We also demonstrate that population inversion in the artificial atom can be detected without measuring coherent radiation output of the maser. For that purpose, the system should operate as a three-terminal heat transport device. The hallmark of population inversion is the influx of heat power into the weakly coupled output terminal even though its temperature exceeds the temperatures of the two other terminals. The proposed method of on-chip conversion of heat into microwave radiation and control of energy-level populations by heating provide additional useful tools for circuit quantum electrodynamics experiments.
A Semantic Model for Security Evaluation of Information Systems

Modern information systems are characterized by huge security related data streams. For cyber security management in such systems, novel models and techniques for efficient processing of these data streams are required. The paper considers development and application of a semantic model for security evaluation. The proposed model is represented as the ontology of metrics that is based on the relations between sources of security related data, primary features of initial security data and goals of security evaluation. The set of hierarchically interconnected security metrics is mapped to the data features and security evaluation goals. The relations between these metrics within the proposed ontology provide the basis for security evaluation technique. The paper introduces the proposed ontology and its foundations, and briefly describes the developed technique. The analysis of data in the open sources is conducted, and the case study is provided to show applicability of the approach.
Bilimsel Makalelerin Atıf Sayısı Tahmini

Even though measuring the impact of scientific papers is not a straightforward process, their citation counts play a significant role in this determination.Citation count of a paper, however, is not available until the paper gets published and a substantial amount of time passes until it spreads through the community.To overcome this issue, we relax the problem by building a deep learning model that predicts whether a paper will receive at least one citation in a one-year interval after its publication.Our model employs Long Short-Term Memory (LSTM) to capture the relationship between word sequences.In our study, we also analyze the effect of using the abstract versus full-text of papers over performance.We utilize publicly available datasets in our experiments: Kaggle for the full-text of papers, and Microsoft Academic Graph for extracting the abstract, metadata features and the initial year citation counts of papers.Our obtained results show that the use of full-text leads to higher accuracy, yet with an enormous trade-off on training time.Additionally, paper abstracts are easier to access as compared to the full-text.Finally, our model predicts that this paper will receive at least one citation during its initial year of publication.
Elaphurus: Ensemble Defense Against Fraudulent Certificates in TLS

Recent security incidents indicate that certificate authorities (CAs) might be compromised to sign certificates with fraudulent information. The fraudulent certificates are exploited to launch successful TLS man-in-the-middle (MitM) attacks, even when TLS clients strictly verify the server certificates. Various security-enhanced certificate verification schemes have been proposed to defend against fraudulent certificates, such as Pinning, CAge, CT, DANE, and DoubleCheck. However, none of the above schemes perfectly solves the problem, which hinders them from being widely deployed. This paper analyzes these schemes in terms of security, usability and performance. Based on the analysis, we propose Elaphurus, an integrated security-enhanced certificate verification scheme on the TLS client side. Elaphurus is designed on top of Pinning, while integrating other schemes to eliminate their disadvantages and improving the overall security and usability. We implement the prototype system with OpenSSL. Experimental results show that it introduces a reasonable overhead, while effectively enhancing the security of certificate verification.
DEVELOPMENT OF A BLENDED COURSE FOR ONLINE TEACHING: PROCESS AND OUTCOMES

In this study, we outline the different steps and strategies followed to develop an online formation program aimed to higher education teachers. The aim of this program is to provide with teaching competencies to face the challenges of the next decade’s classroom, with a special focus on the role of digital technology in learning environments, through a combination of a self-managed course with a guided on-site training with real cases. A multicultural, multidisciplinary team conceives this blended learning format, which applies a storytelling approach for content generation and communication. A detailed description of the different factors and stages followed to undertake the project is presented, together with a series of recommendations to face similar activities in the applied teaching and educational innovation field. Specifically, the importance of an appropriate project design, management and timing is stressed. In this way, we contribute with the diffusion of an innovative, hybrid program to develop digital competences in higher education practitioners and a selection of criteria to undertake other supranational projects that count on a wide reach and follow a didactical approach.
Smart Government E-Services for Indian Railways Using Twitter

The research area of social media platform for e.g. Twitter is being regarded as most popular social media platform not only to share and disseminate the information but also raising complaints/grievances from citizens. In fact, high velocity, veracity and variety of real time data, it is very hard to analyze the data related to citizen’s complaints and problems through manual processing (Agarwal et al. in CoDS-COMAD ‘18 Proceedings of the ACM India joint international conference on data science and management of data. ACM, pp 67–77, 2018 [1]). So there is a need to filter relevant data with automation which requires some actions by concerned authority as a part of Smart Governance. Smart Governance demands distinguishing the appreciations, praises, issues, problems and grievances posted at social platform by civilians to inform government authorities. It also includes facilitating public agencies to respond to these problems, issues, complaints so that citizen’s services can be improved without delay. Thus this paper proposes intelligent techniques to mining public citizens’ complaints and grievances from user-generated contents. Since Twitter considered as most popular social media platform which increases the chances of immediate action and fast processing of their request and grievances by the relevant government department or authorities excellent.
Effect of Header-based Features on Accuracy of Classifiers for Spam Email Classification

Emails are an integral part of communication in today's world.But Spam emails are a hindrance, leading to reduction in efficiency, security threats and wastage of bandwidth.Hence, they need to be filtered at the first filtering station, so that employees are spared the drudgery of handling them.Most of the earlier approaches are mainly focused on building content-based filters using body of an email message.Use of selected header features to filter spam, is a better strategy, which was initiated by few researchers.In this context, our research intends to find out minimum number of features required to classify spam and ham emails.A set of experiments was conducted with three datasets and five Feature Selection techniques namely Chi-square, Correlation, Relief Feature Selection, Information Gain, and Wrapper.Five-classification algorithms-Naïve Bayes, Decision Tree, NBTree, Random Forest and Support Vector Machine were used.In most of the approaches, a trade-off exists between improper filtering and number of features.Hence arriving at an optimum set of features is a challenge.Our results show that in order to achieve the objective of satisfactory filtering, minimum 5 and maximum 14 features are required.
Accurate pedestrian localization in overhead depth images via Height-Augmented HOG

We tackle the challenge of reliably and automatically localizing pedestrians in real-life conditions through overhead depth imaging at unprecedented high-density conditions. Leveraging upon a combination of Histogram of Oriented Gradients-like feature descriptors, neural networks, data augmentation and custom data annotation strategies, this work contributes a robust and scalable machine learning-based localization algorithm, which delivers near-human localization performance in real-time, even with local pedestrian density of about 3 ped/m2, a case in which most stateof- the art algorithms degrade significantly in performance.
Uniform-in-time weak error analysis for stochastic gradient descent algorithms via diffusion approximation

Diffusion approximation provides weak approximation for stochastic gradient descent algorithms in a finite time horizon. In this paper, we introduce new tools motivated by the backward error analysis of numerical stochastic differential equations into the theoretical framework of diffusion approximation, extending the validity of the weak approximation from finite to infinite time horizon. The new techniques developed in this paper enable us to characterize the asymptotic behavior of constant-step-size SGD algorithms for strongly convex objective functions, a goal previously unreachable within the diffusion approximation framework. Our analysis builds upon a truncated formal power expansion of the solution of a stochastic modified equation arising from diffusion approximation, where the main technical ingredient is a uniform-in-time weak error bound controlling the long-term behavior of the expansion coefficient functions near the global minimum. We expect these new techniques to greatly expand the range of applicability of diffusion approximation to cover wider and deeper aspects of stochastic optimization algorithms in data science.
An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine

The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguishable features in fuzzy groups distinguishable. Fault feature separability is very important to improve the fault diagnosis accuracy. In addition, an effective classier can improve the precision and the time taken. With less computational complexity and a simpler process, the ELM algorithm has a fast speed and a good classification performance. The effectiveness of the proposed method is verified by simulation. The simulation results show the ELM-based algorithm classifier with the circle model can enhance precision and reduce time taken by about 80% in comparison with other methods for analog circuit fault diagnosis. To sum up, this proposed method offers a fault diagnosis method that reduces the complexity in generating fault features, improves the isolation probability of faults, speeds up fault classification, and simplifies fault testing.
A Novel Technique of Software Cost Estimation Using Flower Pollination Algorithm

Estimating Cost of Software (ECS) is a very essential aspect of a software development life cycle. Accurate estimations are needed in terms of person month and development time for software projects. Several project estimation techniques have been developed such as parametric and non-parametric. Estimation by parametric is one of the convenient methods in the field of software engineering. However, parametric approaches used in the development of software cost estimation is not able to handle the definite data in an explicit and accurate way. Different computational intelligence algorithms like machine learning, evolutionary algorithms, and swarm intelligence algorithms have been applied to optimize the parameters of various ECS model. But however, accurate cost estimation is still a big issue in ECS. In this work, a Flower Pollination Algorithm (FPA) is proposed to optimize the parameters of a Constructive Cost Model II (COCOMO-II) via using a standard Turkish industry dataset. Experimental results demonstrate that the proposed algorithm gives a better estimation as compared to existing approaches such as the Bat algorithm and original COCOMO-II in terms of Manhattan distance (MD) and mean magnitude of relative errors (MMRE).
Leakage-Resilient Revocable Identity-Based Signature with Cloud Revocation Authority

Very recently, side-channel attacks have threatened all traditional cryptographic schemes.Typically, in traditional cryptography, private/secret keys are assumed to be completely hidden to adversaries.However, by side-channel attacks, an adversary may extract fractional content of these private/secret keys.To resist side-channel attacks, leakage-resilient cryptography is a countermeasure.Identity-based public-key system (ID-PKS) is an attractive public-key setting.ID-PKS settings not only discard the certificate requirement, but also remove the construction of the public-key infrastructure.For solving the user revocation problem in ID-PKS settings, revocable ID-PKS (RID-PKS) setting has attracted significant attention.Numerous cryptographic schemes based on RID-PKS settings have been proposed.However, under RID-PKS settings, no leakage-resilient signature or encryption scheme is proposed.In this article, we present the first leakage-resilient revocable ID-based signature (LR-RIBS) scheme with cloud revocation authority (CRA) under the continual leakage model.Also, a new adversary model of LR-RIBS schemes with CRA is defined.Under this new adversary model, security analysis is made to demonstrate that our LR-RIBS scheme with CRA is provably secure in the generic bilinear group (GBG) model.Finally, performance analysis is made to demonstrate that our scheme is suitable for mobile devices.
Penerapan Metode Rational Unified Process (RUP) Dalam Pengembangan Sistem Informasi Medical Check Up Pada Citra Medical Centre

The Rational Unified Process (RUP) method is an iterative and incremental software development method that focuses on architecture. The RUP method will be used in the development of a medical check-up information system on a Citra medical center because this method is proactively able to resolve the project risks associated with the client's evolving requirements requiring careful change request management. Less time is required for integration as the process of integration goes on throughout the software development life cycle and the development time required is less due to using of components over and over. RUP involves The client’s in The system development process, so the result will be accordance with client wishes. The system built is expected to make it easier for employees to manage patient data from anywhere and anytime.
Attribute-Based Keyword Search from Lattices

Attribute-based keyword search (ABKS) is a special case of public key encryption with keyword search (PEKS) which allows fine-grained control of the search ability and can be further categorized into key-policy ABKS (KP-ABKS) and ciphertext-policy ABKS (CP-ABKS). In a KP-ABKS (resp., CP-ABKS) scheme, a trapdoor that is associated with an access policy f (resp., an attributes string $$\mathbf {x}$$) can only be used to search over ciphertexts that is associated with an attributes string $$\mathbf {x}$$ (resp., an access policy f) if $$f(\mathbf {x})=0$$. As ABKS is very useful in the era of big data, many researchers have been devoted to design ABKS schemes with different features, but almost all the known schemes are based on the traditional number-theoretical assumptions such as Factoring or Discrete Logarithm, and thus are insecure against quantum adversaries. In this paper, we propose a lattice-based KP-ABKS scheme supporting circuit policy of any predetermined polynomial depth. Our scheme is provably secure against chosen keyword attacks and keyword guessing attacks under the DLWE and ISIS assumptions in the random oracle model. By using a universal circuit, our scheme can also be converted into a CP-ABKS scheme.
Cloud Computing Adoption at Higher Educational Institutions in the KSA for Sustainable Development

Rapid changes in the advancements of information and communication technologies (ICT) have prompted HigherEducational Institutions (HEIs) to enhance teaching and learning.Over the years, cloud computing (CC) has become an emerging and adoptable paradigm in many industries including healthcare, finance, and law with its promising benefits.This trend is also growing in the field of education around the globe.Due to its inherent qualities of reliability, scalability, flexibility and reasonable cost, cloud is the solution that addressed the accessibility issue for quality education.CC plays an important role and will have major impacts on HEIs of the Kingdom of Saudi Arabia (KSA) in the near future.HEIs are used to utilize the benefits of CC based services provided by the cloud service providers (CSPs).The CSP can be owned by the KSA government, private, or third-party vendors.By using cloudbased services at HEIs, staff, faculty, and students can utilize its services to perform various academic responsibilities on demand.This paper aims to adopt CC for HEIs and explore the prominent features and potential benefits of adopting cloud services in the HEIs of KSA.This paper also reveals numerous challenges, impacts, and major issues involved in adopting cloud services for HEIs.
Multiple-Instance Learning Approach via Bayesian Extreme Learning Machine

Multiple-instance learning (MIL) can solve supervised learning tasks, where only a bag of multiple instances is labeled, instead of a single instance. It is considerably important to develop effective and efficient MIL algorithms, because real-world datasets usually contain large instances. Known for its good generalization performance, MIL based on extreme learning machines (ELM-MIL) has proven to be more efficient than several typical MIL classification methods. ELM-MIL selects the most qualified instances from each bag through a single hidden layer feedforward network (SLFN) and trains modified ELM models to update the output weights. This learning approach often performs susceptible to the number of hidden nodes and can easily suffer from over-fitting problem. Using Bayesian inferences, this study introduces a Bayesian ELM (BELM)-based MIL algorithm (BELM-MIL) to address MIL classification problems. First, weight self-learning method based on a Bayesian network is applied to determine the weights of instance features. The most qualified instances are then selected from each bag to represent the bag. Second, BELM can improve the classification model via regularization of automatic estimations to reduce possible over-fitting during the calibration process. Experiments and comparisons are conducted with several competing algorithms on Musk datasets, images datasets, and inductive logic programming datasets. Superior classification accuracy and performance are demonstrated by BELM-MIL.