Reviewerly/exHarmony huggingface dataset:
Development of the African Digital Health Library (ADHL): Experience at the Medical Library, University of Ibadan, Nigeria

The African Digital Health Library, (ADHL) is a collaborative project involving five African institutions namely Bamako University of Science and Technology, Kenya Methodist University, Universities of Ibadan, Nairobi, Zambia, and Zimbabwe. The University of Ibadan Nigeria component of the ADHL (ADHL-UI) is described in this paper. Funded by the Office of Global AIDS Coordinator of the United States Department of State, the goal of ADHL-UI is to store, preserve, make accessible through an institutional repository, theses and dissertations by postgraduate students of the institution’s College of Medicine. The ADHL-UI has over 1000 theses and dissertations accessible on the internet. Creation of the ADHL-UI, a repository of theses and dissertations in the Medical Library, University of Ibadan, is a major contribution to research development and preservation of institutional scholarly heritage. Effective use by students and faculty will help in reducing duplication of research efforts and create visibility for the authors and the institution.
Developing Defense Strategies from Attack Probability Trees in Software Risk Assessment

Since the introduction of the Measuring Instruments Directive 2014/32/EU, prototypes of measuring instruments subject to legal control in the European Union must be accompanied by a risk assessment, when being submitted for conformity assessment.Taximeters, water meters, electricity meters or fuel pumps form the basis for the economic sector usually known as Legal Metrology, where the development towards cheaper allpurpose hardware combined with more sophisticated software is imminent.Therefore, a risk assessment will always have to include software-related issues.Hitherto, publications about software risk assessment methods lack an efficient means to derive and assess suitable countermeasures for risk mitigation.To this end, attack trees are used in related research fields.In this paper, defense probability trees are derived from attack probability trees, well-suited to the requirements of software risk assessment and used to identify optimal sets of countermeasures.The infamous Meltdown vulnerability is used to highlight the experimental application of the method.
Caption This! Best Practices for Live Captioning Presentations

We demonstrate the effectiveness of straightforward strategies using widely available auto-captioning tools to greatly improve accessibility of jargon-rich content.
A Client-centric Approach to Transactional Datastores

Traditional brick-and-mortar services are increasingly moving online and companies rely on ever larger data analytics to optimise their business logic. Similarly, most medical practices favour electronic health records over paper documents: 84% of American hospitals store medical records electronically [19], an 8-fold increase since 2008. In this data-driven world, data is money. It must be collected efficiently, even as it spans multiple heterogeneous, geo-distributed sources. Data must be stored reliably, even in the presence of failures. As the sensitivity of the data being stored increases, so does the need to store it securely in the presence of human attacks. Data must also be accessed consistently, even under high load.
Graphical Analysis of the Progression of Atrial Arrhythmia Using Recurrent Neural Networks

Pacemaker logs are used to predict the progression of paroxysmal cardiac arrhythmia to permanent atrial fibrillation by means of different deep learning algorithms.Recurrent Neural Networks are trained on data produced by a generative model.The activations of the different nets are displayed in a graphical map that helps the specialist to gain insight into the cardiac condition.Particular attention was paid to Generative Adversarial Networks (GANs), whose discriminative elements are suited for detecting highly specific sets of arrhythmias.The performance of the map is validated with simulated data with known properties and tested with intracardiac electrograms obtained from pacemakers and defibrillator systems.
Exploring the Use of Lexical and Psycho-Linguistic Resources for Sentiment Analysis

Twitter data have been used for monitoring customer opinions in different domains. In this paper, we investigated the role of a wide set of lexical and psycho-linguistic resources for discovering a set of features that can be potentially useful for this task. Different experiments were carried out, the obtained results allow us to demonstrate that by using a small set of features it is possible to obtain competitive results against more complex approaches. The experimental evaluation was carried out on a collection of tweets corresponding to the airline domain. The obtained results allow us to observe a significant reduction in the vector representation dimension, maintaining satisfactory results using lexical resources for capturing different kinds of information to perform sentiment analysis in tweets on the aforementioned domain.
Security Threats in Cloud Computing: Trend and Challenges

Recently due to the increasing popularity of cloud computing, the people are attractingtheir services and offers without knowing the security threats. Users think that that cloud is moresecure as compared to any other services. This fact leads to serious problems for users. This is whythe security of the cloud has become a major issue. In this paper, we investigate the recent and mostcommon threats of cloud computing. After a detailed investigation, we discuss the seven majorthreats related to cloud computing. These risks are related to cloud computing and are the mainguidance for risk analysis for organizations or customers to switch their services on the cloudcomputing platform. The organization or customers must know about these threats before adaptingany cloud service or interface. This paper will guide the new researchers in the field of cloudcomputing and its security services to design more secure and reliable solutions in the future.
National and International Issues of Cyber Security

Abstract Improving cyber security is the purpose of all states. Military-industrial complex is in need of creating innovative protection systems for their cyber space. A lack of such security systems can lead to the global disaster, using nuclear weapons. Today’s legal regulation of the national personal data still does not provide for legal regulation of user data which can be obtained via gadgets fitted with cameras, speakers, applications. Apart from information leakage as a result of hacker attacks, and accidental cyber leaks, the great problem is related to unlawful leakage and data stolen by company employees. Although the legislation of personal data protection is improving with allowance for precedents in this sphere leading to cybercrimes, an amount of unlawful use of personal data by company employees do not decrease due to a lack of accountability and behavior code in the sphere of personal data processing.
Fuzzy logic in controlling the forest fire - level forecast warning signage

Forest fires are always a problem around the world because of its great harm. Especially in Vietnam, the prevention and detection of forest fires are mainly based on the patrolling forest rangers; the warning board is not automatically controlled. In this article, we will present the design of an automatic controller, which was applied fuzzy logic to control the forest fire - level forecast warning signage. The controller relies on regional meteorological information to control the signage on the spot, and to directly inform the manager about the forest fire forecasting situation via text message. The experienced results of some forest protection units in Thai Nguyen prove that the application of fuzzy logic has met the requirements of the forest protection sector in improving the forest fire forecasting system.
Comprehensive Study of Various Techniques for Object Detection and Tracking in Video

The methods for object detection helps in identifying the presence of an object whether in an image or a video, in the area of computer vision. They are helpful in many applications such as man-machine interfaces, traffic monitoring, transportation, manufacturing, target tracking, robotics, health care domain and various indoor-outdoor based video surveillance application. Many algorithms are used for implementing methods for object detection, classification, representation & essentially tracking. This paper has focused on various methods for object detection and tracking. The efficacy of this paper work is shown in discussion for various methods.
Attentional Colorization Networks with Adaptive Group-Instance Normalization

We propose a novel end-to-end image colorization framework which integrates attention mechanism and a learnable adaptive normalization function. In contrast to previous colorization methods that directly generate the whole image, we believe that the color of the significant area determines the quality of the colorized image. The attention mechanism uses the attention map which is obtained by the auxiliary classifier to guide our framework to produce more subtle content and visually pleasing color in salient visual regions. Furthermore, we apply Adaptive Group Instance Normalization (AGIN) function to promote our framework to generate vivid colorized images flexibly, under the circumstance that we consider colorization as a particular style transfer task. Experiments show that our model is superior to previous the state-of-the-art models in coloring foreground objects.
Global legal response to coronavirus (COVID-19) and its impact: perspectives from Nigeria, the United States of America and the United Kingdom

Global COVID-19 legislation contains the spread of the disease. However, the Nigerian government's response to the spread of the virus suffers some technical flaws. Consequently, this paper asks how Nigeria’s response to the pandemic compares with those of the United States of America (US) and the United Kingdom (UK). Qualitatively, it seeks to substantiate the proposition that Nigeria's response relies too heavily on executive directives and extra-judicial controls, leading to human rights abuses. Even worse is the absence of specific provisions in the Nigerian legislation detailing the application of remote hearing to facilitate redress arising from these infractions comparable to what is obtainable in the US and UK. The paper highlights the several loopholes demonstrated by Nigeria's legal response to the COVID-19 pandemic, its impact, and the lessons the Nigerian government can adapt based on the US and UK experiences.
STUDYING AND ANALYZE THE FACTORS THAT AFFECT COMPLIANCE DENTIST RECOMMENDATIONS FROM PARENTS OF CHILD PATIENTS

The aim: Investigate the factors that influence the tendency of parents to follow the advice of a dentist on the way to improving the dental health of their children and build a prognostic model.Materials and methods: Materials: we analyzed 406 questionnaires (specially created by the author), which contain the answers of parents of schoolchildren of Poltava. Methods: questionnaires, medical and statistical, modeling.Results: The statistical processing of the questionnaires revealed a number of factors those that affect parents' compliance with the advice of a dentist, increasing or decreasing the chances of it: low sanitary and hygienic awareness of parents; non-observance of hygiene rules by parents; parents do not teach the child to care for the oral cavity;opinion of the parents that only state must take care of children's health; the recommendations of the dentist wasn`t received; child had never visited a dentist for prevention. The operational characteristics of the obtained prognostic model: the area under the curve is 0,762, which indicates a good predictability of the model. Sensitivity is 82,2%, specificity - 79,8%.Conclusions: The data obtained as a result of our study and the constructed prognostic model: they indicate low sanitary and hygienic awareness of parents, paternalism of their thinking (shifting responsibility to someone, unwillingness to take responsibility), lack of medical and hygienic knowledge, as a result of which they do not apply them to themselves and, of course, to their children. The solution to this problem is possible only through informational and explanatory activities with the involvement of modern methods of communication and people who have passed appropriate training.
DPS-AA: Intranet Migration Strategy Model for Clouds

Intranets & Intranet-wares have become a central junction/platform for implementing the organization's specific work culture like computing, communication and collaboration.In order to ensure smooth and effective communication, computing and collaboration among employees, intranets play an important role.For vertical and horizontal management, we use intranet-wares for directing, reporting, collaborating, socializing, communicating and meeting or discussing the professional and social issues.Today, cloud-based computing, communication, and collaboration have created new frontiers and emerging paradigms towards re-engineering of work cultures in the organizations.In order to enhance the performance with extended features, next-generation computing, communication, and collaboration the intranet needs redesign and migration strategy over alternative technology platforms.This research paper tries to answer the research questions that how an alternative technology strategy or pathway can be explored for enhancing the performance and extending the features of the exiting designs of educational Intranets.Further; how an on-premise intranet can be migrated over cloud platforms with enhanced performance and extended/add-on features.After analysis of collected facts, understanding the issues, challenges and limitations of the existing state of art intranets, a strong need for performance enhancement and add on features was observed for Intranets.The study deeply investigated and analyzed the issues, challenges and limitations i.e. features and performances of the current state of the art of the intranets in general and on-premise Intranet of AMU in specific.Finally, an Intranet Migration Strategy Model over Hybrid Cloud was designed and developed using SaaS (i.e.AMU CloudNet).In this study, the Interact Intranet was used for designing and demonstrating the functional prototype of intranet show that how computing, communication and collaboration services can be enhanced with anytime, anywhere and boundary-less access.
Educational Counseling Programs for Students with Disabilities during the Pandemic

The present empirical and descriptive study aims to highlight the contribution of educational programs to the academic progress of adult students with Special Educational Needs and disabilities (SENs/Ds) during the pandemic period (World Health Organization, 2020 b). The main hypothesis investigated whether these interventions for learning problems helped them to manage the personal difficulties that hinder the smooth process of obtaining the degree. Data were collected with empirical descriptions according to the observation and the intervention methodology for SENs/Ds and were recorded with some protocols with physical presence or by distance learning. The pedagogical tool for the Special Educational Programs was applied with emphasis on the Targeted Individual Counseling Structured, Differentiated, Didactic Intervention without Exclusion interventions. The recordings followed the linear layout of the interventions that students receive at the University upon their request. The results showed that educational programs can temporarily relieve students in dealing with academic difficulties in work exams. Isolation, social distance and emotion management also proved to be extremely difficult for most students.
Digital Election Observation: Regulatory Challenges around Legal Online Content

Abstract Between public debates about ‘hacking’ elections, so‐called ‘fake news’ and online disinformation campaigns, it has become hard to imagine what free and fair elections in a digital environment could look like. This challenge is particularly pronounced for election observers who monitor free and fair elections. How should election observers fulfil this task when reliable data in online media campaigns are often not even available to media regulators? The following article provides a brief overview of existing challenges around online content regulation and how these apply to elections and election observation. It then considers where resources for digital electoral observation exist and how most effectively to build on these before, in conclusion, discussing next steps and potential opportunities to develop digital election observation further.
LDNN: Linguistic Knowledge Injectable Deep Neural Network for Group Cohesiveness Understanding

Group cohesiveness reflects the level of intimacy that people feel with each other, and the development of a dialogue robot that can understand group cohesiveness will lead to the promotion of human communication. However, group cohesiveness is a complex concept that is difficult to predict based only on image pixels. Inspired by the fact that humans intuitively associate linguistic knowledge accumulated in the brain with the visual images they see, we propose a linguistic knowledge injectable deep neural network (LDNN) that builds a visual model (visual LDNN) for predicting group cohesiveness that can automatically associate the linguistic knowledge hidden behind images. LDNN consists of a visual encoder and a language encoder, and applies domain adaptation and linguistic knowledge transition mechanisms to transform linguistic knowledge from a language model to the visual LDNN. We train LDNN by adding descriptions to the training and validation sets of the Group AFfect Dataset 3.0 (GAF 3.0), and test the visual LDNN without any description. Comparing visual LDNN with various fine-tuned DNN models and three state-of-the-art models in the test set, the results demonstrate that the visual LDNN not only improves the performance of the fine-tuned DNN model leading to an MSE very similar to the state-of-the-art model, but is also a practical and efficient method that requires relatively little preprocessing. Furthermore, ablation studies confirm that LDNN is an effective method to inject linguistic knowledge into visual models.
A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments

Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results.
Surgical Hand Gesture Prediction for the Operating Room.

Technological advancements in smart assistive technology enable navigating and manipulating various types of computer-aided devices in the operating room through a contactless gesture interface. Understanding surgeon actions is crucial to natural human-robot interaction in operating room since it means a sort of prediction a human behavior so that the robot can foresee the surgeon's intention, early choose appropriate action and reduce waiting time. In this paper, we present a new deep network based on Convolution Long Short-Term Memory (ConvLSTM) for gesture prediction configured to provide natural interaction between the surgeon and assistive robot and improve operating-room efficiency. The experimental results prove the capability of reliably recognizing unfinished gestures on videos. We quantitatively demonstrate the latter ability and the fact that GestureConvLSTM improves the baseline system performance on LSA64 dataset.
Majorana stellar representation for mixed-spin (s, 1/2) systems*

By describing the evolution of a quantum state with the trajectories of the Majorana stars on a Bloch sphere, Majorana's stellar representation provides an intuitive geometric perspective to comprehend a quantum system with high-dimensional Hilbert space. However, the problem of the representation of a two-spin coupling system on a Bloch sphere has not been solved satisfactorily yet. Here, we present a practical method to resolve the problem for the mixed-spin $(s, 1/2)$ system. The system can be decomposed into two spins: spin-$(s+1/2)$ and spin-$(s-1/2)$ at the coupling bases, which can be regarded as independent spins. Besides, we may write any pure state as a superposition of two orthonormal states with one spin-$(s+1/2)$ state and the other spin-$(s-1/2)$ state. Thus, the whole state can be regarded as a state of a pseudo spin-$1/2$. In this way, the mixed spin decomposes into three spins. Therefore, we can represent the state by $(2s+1)+(2s-1)+1=4s+1$ sets of stars on a Bloch sphere. Finally, to demonstrate our theory, we give some examples that indeed show laconic and symmetric patterns on the Bloch sphere, and unveil the properties of the high-spin system by analyzing the trajectories of the Majorana stars on a Bloch sphere.
Application of Method of Differential Inequalities to Bounding the Rate of Convergence for a Class of Markov Chains

We consider the linear system of differential equations $$\frac{d\mathbf{p}}{dt}=A(t)\mathbf{p}$$ , which is the forward Kolmogorov system, for a class of Markov chains with ‘batch’ births and single deaths. We apply the method of differential inequalities for obtaining bounds on the rate of convergence for the system. A specific queueing model is considered and the corresponding limiting characteristics are computing.