Reviewerly/exHarmony huggingface dataset:
Informetric Portrait of Elinor Ostrom, the Nobel Laureate in the Field of Economic Sciences

The publication productivity of Elinor Ostrom, the renowned American Political economist and the first woman laureate to win the most aspired highest prize that is the Nobel Prize in Economics in 2009 for her analysis of economic governance is analysed. The present study has analysed her 197 publications as indexed in Scopus using bibliometric methods covering the period 1965-2018. It is observed that during her 54 years of publication she published average 3-4 works each year. The years 2006, 2008- 2013 accounts for 103 publications i.e. 52.28% and they are the most productive years in her life. She published 85 single-authored papers and 112 multiauthored papers. James Michael Walker who co-authored 19 publications is the most prominent collaborator. Natural resources management is the prominent area of her research. Ostrom used 116 channels prominently being the journal articles covering 119 (60%) of her publications. Ostrom’s works had high reception which can also be seen from the citations received; 50552 citations as per Scopus. Her Recency Index is nearly 0.47 which indicates that nearly 47% of her works are cited in the last five years. Ostrom’s works are an inspiration for the future Nobel aspirants and researchers in economics because she designed a new principle and argued with proof that common resources are best utilised and best managed when they are managed by the localities of the areas where those resources are found. This provided an opportunity for the common people to earn a living as well as helped in the best utilisation of the resources which is a unique idea. Her works therefore opened up avenues to find out the new resources of various areas and continues to be important in the future. The bibliometric analysis attempts to draw some insights of her interesting and important work.
RAitc: Securely Auditing the Remotely Executed Applications

One of the most important security challenges in remote computing (e.g., cloud computing) is protecting users' applications running on the service platform from malicious attacks.Because remote users have little control over the platform, a malicious platform manager or platform-sharing guest acting as an adversary can easily create an untrustworthy execution environment.Prior studies have leveraged trusted third party (TTP)-based and trusted execution environment (TEE)-based approaches to mitigate such security issues, but these approaches still provide little transparency from the user's perspective.To address this challenge, we present a remote auditing approach based on an identified trust chain (RAitc) to analyze the correctness of remotely loaded applications.The chain is constructed with two goals: the first is to identify the remote platform to ensure that the user has a designated service system; the second is to build a trust chain from the user to the designated platform via verifiable computing-based module measurements and kernel-based application auditing.RAitc achieves a higher guarantee of safety in securely monitoring and verifying the integrity of remote applications executed by users.In addition, RAitc is both easier and more flexible for the extension of the trust base.Our implementation of RAitc protects users' remote execution environments while requiring an acceptable overhead on the target system in application auditing.We rigorously and comprehensively evaluated the effectiveness and performance of RAitc.The results show that RAitc performs effectively and has acceptable resource consumption.
Application of the Last Planner® System in a Sports Infrastructure Project in Peru

The Last Planner System (LPS) is been applied in Peru since the 2000s for many General Contractors (GC), mainly in the concrete frames phase of building projects.However, most of the problems with variability and coordination comes up during the MEP (Mechanical, Electrical, and Plumbing) phase and finishing works.The following paper sums up the application of The Last Planner on a large sports infrastructure project in Peru for the Pan-American Games focused on MEP phase and finishing works.It offers an overview of the implementation of LPS and its impact on the indicators of the project.This implementation took 27 weeks and was divided into two phases.The first phase took 10 weeks.Here, subcontractors discovered and learned about LPS.After this process the team evaluated the level of knowledge and identified opportunities to improve it.With this diagnose, the team implemented a set of measures to increase the level of LPS on the project.Finally, this implementation helped the project to reduce the variability (increase the Percentage of Complete Plan) and increase SPI (Schedule Performance Index), making it possible to deliver on time.
GPU Accelerated Genetic Algorithm with Sequence-based Clustering for Ordered Problems

The island model allows genetic algorithms to effectively maintain diversity through migration between multiple independent populations.Due to its flexibility and modularity, it is commonly employed in distributed and parallel implementations, particularly in recent trends in leveraging the massively parallel cores in GPUs.However, the efficiency and effectiveness of the island model can be considered as its ability to manage its global and local search while minimising the overlap of islands searching in the same area of the solution space.This paper introduces a GPU accelerated island-model genetic algorithm that conducts global search by organising its populations into islands according to the similarity in genotype sequences.Local search is managed through adaptive mechanisms designed to maintain population diversity.The characteristics of the proposed genetic algorithm are investigated with encouraging results demonstrating its robustness and scalability when solving ordered optimisation problems.
Uniform labelled calculi for preferential conditional logics based on neighbourhood semantics

Abstract The preferential conditional logic $ \mathbb{PCL} $, introduced by Burgess, and its extensions are studied. First, a natural semantics based on neighbourhood models, which generalizes Lewis’ sphere models for counterfactual logics, is proposed. Soundness and completeness of $ \mathbb{PCL} $ and its extensions with respect to this class of models are proved directly. Labelled sequent calculi for all logics of the family are then introduced. The calculi are modular and have standard proof-theoretical properties, the most important of which is admissibility of cut that entails a syntactic proof of completeness of the calculi. By adopting a general strategy, root-first proof search terminates, thereby providing a decision procedure for $ \mathbb{PCL} $ and its extensions. Finally, semantic completeness of the calculi is established: from a finite branch in a failed proof attempt it is possible to extract a finite countermodel of the root sequent. The latter result gives a constructive proof of the finite model property of all the logics considered.
Detecting Attacks on a Water Treatment System Using Oneclass Support Vector Machines

Critical infrastructure assets such as power grids and water treatment plants are monitored and managed by industrial control systems. Attacks that leverage industrial control systems to disrupt or damage infrastructure assets can impact human lives, the economy and the environment. Several attack detection methods have been proposed, but they are often difficult to implement and their accuracy is often low. Additionally, these methods do not consider the digital forensic aspects. This chapter focuses on the use of machine learning, specifically one-class support vector machines, for attack detection and forensic investigations. The methodology is evaluated using a water treatment testbed, a scaled-down version of a real-world industrial water treatment plant. Data collected under normal operations and attacks are used in the study. In order to enhance detection accuracy, the water treatment process is divided into sub-processes for individual one-class support vector machine model training. The experimental results demonstrate that the trained sub-process models yield better detection performance than the trained complete process model. Additionally, the approach enhances the efficiency and effectiveness of forensic investigations.
Changing the Educational Paradigm in Post-Pandemic World: Possibilities and Risks of Artificial Intelligence Using

Modern technical innovations are marked by an extremely powerful symbiosis with scientific achievements. Today, there are a number of technologies that can reproduce human thinking and such skills as understanding complex information, drawing independent conclusions, and the ability to engage in meaningful and coherent dialogue. They testify to the possibility of innovative achievements in neuroscience to individually assess the level of concentration and perseverance of students, pace, best time to learn, etc. The robots are not able to inspire despite their computing power, simplicity of interface and ability to store information. They lack empathic abilities. They do not fully promote the realization of social skills. The global pandemic has actualized and intensified the introduction of artificial intelligence in the practice of education. Researchers have expressed a number of concerns that artificial intelligence in exchange for the rapid acquisition and effective use of a variety of information by a person will help to do levelling of the spiritual and emotional sphere of a person, i.e. “human nature in a human being.”
Insider Threat Detection Using Multi-autoencoder Filtering and Unsupervised Learning

Insider threat detection and investigation are major challenges in digital forensics. Unlike external attackers, insiders have privileges to access resources in their organizations and violations of normal behavior are difficult to detect. This chapter describes an unsupervised deep learning framework for detecting insider threats by analyzing system log files. A typical deep neural network can capture normal behavior patterns, but not insider threat behavior patterns because of the presence of small, if any, amounts of insider threat data. For example, the autoencoder unsupervised deep learning model, which is widely used for anomaly detection, requires a dataset containing labeled normal data for training purposes and does not work well when the training dataset contains anomalies. In contrast, the framework proposed in this chapter leverages unsupervised multi-autoencoder filtering to remove anomalies from a training dataset and uses the resulting trained Gaussian mixture model to estimate the distributions of encoded and recognized normal data; data with lower probabilities is identified as insider threat data by the trained model. Experiments demonstrate that the multi-autoencoder-filtered unsupervised learning framework has superior detection performance compared with state-of-the-art baseline models.
Speech Recognition for Task Domains with Sparse Matched Training Data

We propose two approaches to handle speech recognition for task domains with sparse matched training data. One is an active learning method that selects training data for the target domain from another general domain that already has a significant amount of labeled speech data. This method uses attribute-disentangled latent variables. For the active learning process, we designed an integrated system consisting of a variational autoencoder with an encoder that infers latent variables with disentangled attributes from the input speech, and a classifier that selects training data with attributes matching the target domain. The other method combines data augmentation methods for generating matched target domain speech data and transfer learning methods based on teacher/student learning. To evaluate the proposed method, we experimented with various task domains with sparse matched training data. The experimental results show that the proposed method has qualitative characteristics that are suitable for the desired purpose, it outperforms random selection, and is comparable to using an equal amount of additional target domain data.
Power to the People

By designing rack-level architectures, huge improvements can be made for power efficiency over conventional servers, since PSUs will be less oversized, more consolidated, and redundant for the rack versus per server. While the hyperscalers have benefited from these gains in power efficiency, most of the industry is still waiting. The Open Compute Project was started as an effort to allow other companies running datacenters to benefit from the power efficiencies as well. If more organizations run rack-scale architectures in their datacenters, the wasted carbon emissions caused by conventional servers can be lessened.
The spherical-Dirichlet distribution

Abstract Today, data mining and gene expressions are at the forefront of modern data analysis. Here we introduce a novel probability distribution that is applicable in these fields. This paper develops the proposed spherical-Dirichlet distribution designed to fit vectors located at the positive orthant of the hypersphere, as it is often the case for data in these fields, avoiding unnecessary probability mass. Basic properties of the proposed distribution, including normalizing constants and moments are developed. Relationships with other distributions are also explored. Estimators based on classical inferential statistics, such as method of moments and maximum likelihood estimators are obtained. Two applications are developed: the first one uses simulated data, and the second uses a real text mining example. Both examples are fitted using the proposed spherical-Dirichlet distribution and their results are discussed.
Mobile Learning via Mobile Devices in Nigeria Higher Education: Usage Analysis Based on Utaut Model

The recent influx of various technologies has affected all sectors of the human life including education. Mobile learning has emerged with the evolution of mobile devices that has enhanced knowledge sharing via distance education systems. In Nigeria, it has been observed that under-utilization of the technology in higher education institutions (HEIs) is still prevalent. This study investigated the factors hindering the use of mobile devices for mobile-learning by students. Four research questions were formulated based on UTAUT model with nine variables and 391 survey questionnaires were administered on the students in two institutions in Ogun State. Data obtained were analyzed using two multiple regression and path analysis on SPSS 23. Findings showed that the facilitating factor of the mobile device leads other variables on direct effect on the attitude of students towards usage of mobile devices for m-learning. The management of HEIs should provide technical infrastructure supports on the use of mobile devices.
Enhanced Weighted Round Robin Algorithm to Balance the Load for Effective Utilization of Resource in Cloud Environment

Cloud computing contains basically virtualization, networking, distributing computing, software and web services over the cloud.Cloud computing provide basis on demand hosting computing resources and cloud services over the cloud or internet on pay per use basis.Due of high availability, fault tolerance, scalability, management simplicity and low cost Cloud computing currently become best method of computation over large scalable network environment .Efficient load balancing make cloud computing environment more efficient and also get better user satisfaction.The idea of this paper is to propose load balancing algorithm for utilization of resource efficiently and to compare the performance of projected algorithms with well-known load balancing algorithms.The newly proposed algorithm will consider size of cloudlet, expected completion time of tasks by virtual machine and runtime properties of virtual machines to map's the incoming request to virtual machine in impartially and efficiently.The response time of EWRR method is less in comparison of others methods.It has been found that EWRR having the better result in comparison of RR, Throttled, ACO and Hybrid response times which are 0.77, 2.20,8.31,20.82 and 100 respectively.In this paper, by proposing a virtual machine load balancing algorithm that aims to improve the average response time and average processing time of the system in the cloud environment.
On the Use of Deep Active Semi-Supervised Learning for Fast Rendering in Global Illumination

Convolution neural networks usually require large labeled data-sets to construct accurate models. However, in many real-world scenarios, such as global illumination, labeling data are a time-consuming and costly human intelligent task. Semi-supervised learning methods leverage this issue by making use of a small labeled data-set and a larger set of unlabeled data. In this paper, our contributions focus on the development of a robust algorithm that combines active and deep semi-supervised convolution neural network to reduce labeling workload and to accelerate convergence in case of real-time global illumination. While the theoretical concepts of photo-realistic rendering are well understood, the increased need for the delivery of highly dynamic interactive content in vast virtual environments has increased recently. Particularly, the quality measure of computer-generated images is of great importance. The experiments are conducted on global illumination scenes which contain diverse distortions. Compared with human psycho-visual thresholds, the good consistency between these thresholds and the learning models quality measures can been seen. A comparison has also been made with SVM and other state-of-the-art deep learning models. We do transfer learning by running the convolution base of these models over our image set. Then, we use the output features of the convolution base as input to retrain the parameters of the fully connected layer. The obtained results show that our proposed method provides promising efficiency in terms of precision, time complexity, and optimal architecture.
Information System For Monitoring Of Pregnant Mother In Riau Province Health Office

The data collection of pregnant women is the realization of the Birth Planning and Prevention of Complications (P4K) Program in the Riau Provincial Health Office of the Family and Nutrition Health Section. The data collection of pregnant women aims to find out information about pregnant women scattered in 12 regencies and cities in Riau Province including: the number of pregnant women, Tabulin, transportation, blood donors, assisting pregnant women and birth assistance plans. Currently reports and data recording of pregnant women are still done manually using Excel. To make it easier for officers to monitor the health of pregnant women in Riau Province, a Web based health monitoring system for pregnant women was built. This system integrates data on pregnant women in Riau Province starting from the Puskesmas level. This system can help officials in the Health Department monitor and record pregnant women, so that they can assist in planning a safe delivery and preparing for complications for pregnant women.
Forensic Analysis of Third-party Mobile Application

The expansion of the phone's memory space and processing power comprise some of the primary contributing factors to the evolution of digital crime.Therefore, wide adoption and reliance of third-party applications expose individuals to various digital crimes.In this research both qualitative and quantitative methods are used.The recovered data items from the phone's memory were classified and facilitate the description of the recoverable app and user information after the deletion of the social networking app like Facebook.The choice of Facebook as the subject app for investigation arises from the fact that it comprises one of the major social network platforms.Some of the recoverable data in both scenarios include but not limited to text messages, login information, friends' information, and user account details, among others.The research seeks to fill an existing gap concerning the establishment of the location and types of recoverable data after the uninstallation of the Facebook application from a smartphone device.
Authentic Leadership and Openness to Change in Pakistani Service Industry: The Mediating Role of Trust and Transparent Communication

Organizations to engage in strategic change initiatives to remain competitive. Leadership is the top determinant of successful change. This study investigated the factor affecting the employee’s openness to change during process of change and how leadership affects that. We proposed a theoretical framework, modifying (Yue, Men, &amp; Ferguson, 2019) by incorporating Authentic in place of transformational Leadership. We hypothesized that authentic leadership affect organisational trust during change both directly, as well as by inducing transparent communication. Trust would in turn positively affect openness to change. Authentic leadership included 1. Self-Awareness, 2. Relational Transparency, 3. Balanced Processing, and 4. Internalized Moral Perspective, dimensions. Whereas, transparent communication consisted of 1. Participation, 2. Substantiality, 3. Accountability, Factors. Empirical validity was established by conducting a survey using close ended questionnaire. Data was collected from 310 employees working across different service industry in Karachi, and analyzed using confirmatory factor analysis and structured equation modeling. The finding revealed that transparent communication and authentic leadership have significance direct and indirect relationship with employees’ openness to change. Organizational trust has significant relationship with employees’ openness to change during change process. However Authentic leader has weak direct relation with transparent communication and there is no significance relationship between authentic leadership and trust with mediating role of transparent communication. However, trust significantly mediate all the factors of authentic leadership except Self-Awareness, and Openness to Change. Similarly, trust also mediated Substantiality, and Accountability factors of communication, and openness. Lastly, substantiality also offered a partial mediatory role between authentic leadership and trust.
Reflections on Collective Collections

Collective collections are multiple local collections described and/or managed as a single collection. Constructing, understanding, and operationalizing collective collections is an increasingly important aspect of collection management for many libraries. This article presents some general insights about collective collections, drawn from a series of studies conducted by OCLC. These insights identify salient characteristics of many collective collections and serve as a starting point for developing collective collection-based strategies for such library priorities as shared print, digitization, and group-scale discovery and fulfillment.
Design and Randomness Evaluation of a Chaotic Neural Encryption and Decryption Network for TRNG

The prime objective of any cryptographic system is all about exchanging information amidst the expected users without letting any information getting leaked out to others who may have unapproved access to it. Since various public key cryptography techniques has been widely recognized which were based on number theory and they need substantial reckoning power. The procedure involved in forming public key is cumbersome and time-consuming. A common secret key will be generated to solve this problem. In this paper, artificial neural network based chaotic cryptography is presented which proves that the messages are highly protected. The randomness of the encrypted sequences for different input sequences was analyzed and interpreted by using NIST Statistical Test Suite.