Reviewerly/exHarmony huggingface dataset:
Trade Openness And Inflation: Empirical Explanation Of The Nexus In Nigeria

Purpose of the study: The study aims is to examine the effect of trade openness on inflation rate in Nigeria.&#x0D; Methodology: Time series data were collected from secondary sources. EViews10 (statistical software for data analysis) ware employed to analyze the data collected.&#x0D; Findings: The results revealed a cointegrating and one-way Granger causality between inflation rate, and trade openness. In addition, both the short-run and the long-run results demonstrate a significant and negative relationship between inflation rate and trade openness in Nigeria.&#x0D; Application: The study is paramount to the government and policymakers in dealing and taking a decision regarding consumer price index and trade openness in Nigeria. We conclude that the government should work towards full diversification and diversion of the economy from oil export, control, and management of the degree of trade liberalization and the extent to which goods enter the country, and the control of money supplied.&#x0D; Novelty/Originality: The study accorded to debate on the inflation rate, and trade openness in Nigeria looking, at both short-run and long-run effects, before few accessible studies focused on impact, and trade openness was not measured as the value of net export divided by gross domestic product. Finally, the paper contributed to the scanty of the literature.
Efficient travelling-mode quantum key agreement against participant’s attacks

Abstract Quantum key agreement (QKA) is to negotiate a final key among several participants fairly and securely. In this paper, we show that some existing travelling-mode multiparty QKA protocols are vulnerable to internal participant’s attacks. Dishonest participants can exploit a favorable geographical location or collude with other participants to predetermine the final keys without being discovered. To resist such attacks, we propose a new travelling-mode multiparty QKA protocol based on non-orthogonal Bell states. Theoretical analysis shows that the proposed protocol is secure against both external and internal attacks, and can achieve higher efficiency compared with existing travelling-mode multiparty QKA protocols. Finally we design an optical platform for each participant, and show that our proposed protocol is feasible with current technologies.
Design for Participation and Inclusion will Follow : Disabled People and the Digital Society

Digitalization is rapidly taking over all aspects of society, but still there are parts of the population who have to struggle for access to, and to be able to use, the digital resources. Design pr ...
Relation Module for Non-Answerable Predictions on Reading Comprehension

Machine reading comprehension (MRC) has attracted significant amounts of research attention recently, due to an increase of challenging reading comprehension datasets.In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e.g. the recently proposed SQuAD 2.0 task).Our solution is a relation module that is adaptable to any MRC model.The relation module consists of both semantic extraction and relational information.We first extract high level semantics as objects from both question and context with multihead self-attentive pooling.These semantic objects are then passed to a relation network, which generates relationship scores for each object pair in a sentence.These scores are used to determine whether a question is nonanswerable.We test the relation module on the SQuAD 2.0 dataset using both the BiDAF and BERT models as baseline readers.We obtain 1.8% gain of F1 accuracy on top of the BiDAF reader, and 1.0% on top of the BERT base model.These results show the effectiveness of our relation module on MRC.
ARES: An Agile Requirements Specification Process for Regulated Environments

Agile methods have provided significant contributions to Software Engineering. This work presents a new process for Software Requirements Specification, integrating Agile Properties and regulated environments, such as aviation, medical, nuclear and automotive, among others. The Software in Regulated Environments (SRE) involves plan-driven methods with needed documentation to ensure safety, reliability, security, and discipline. This paper proposes a balance between agile and plan-driven methods. We define a new process, which explores and investigates the usage of agile methods in SRE. The scope of this paper is Requirements Engineering, which is considered as a set of activities involved in the management, elicitation, documentation, and maintenance of requirements. The Adile Requirements Specification (ARES) process contains four methods, 13 activities, and some required artifacts to ensure compliance with the following six relevant Software Standards for regulated environments: RTCA DO-178C, IEC 62304:2015, ECSS-E-ST-40C, IEC 61508-3, ISO/IEC/IEEE 12207, and IAEA SSG-39. The process evaluation was performed using two experiments: a Cockpit Display System (CDS) and a Healthcare Information System (HIS). These experiments were measured with appropriate metrics to ensure improvements in Software Requirements Specification and traceability among artifacts. The experimental results revealed that the ARES process works better than the original Scrum for Software in Regulated Environments. The ARES process can also be integrated with traditional software life cycles (Waterfall, V, and Incremental and Iterative), when applied in the Requirements Engineering phase.
Recognition of vehicle make and model in low light conditions

This paper presents a method for vehicle make and model recognition (MMR) in low lighting conditions. While many MMR methods exist in the literature, these methods are designed to be used only in perfect operating conditions. The various camera configuration, lighting condition, and viewpoints cause variations in image quality. In the presented method, the vehicle is first detected, image enhancement is then carried out on the detected front view of the vehicle, followed by features extraction and classification. The performance is then examined on a low-light dataset. The results show around 6% improvement in the ability of MMR with the use of image enhancement over the same recognition model without image enhancement.
Genetic algorithm applied to remove noise in DICOM images

AbstractAbstractThe challenge of noise attenuation in images has led to extensive research on improved noise reduction techniques, preserving important image characteristics, improving not only visual perception, but also enabling the use for special purposes, such as in medicine to increase clarity of medical images. In this paper, a technique for noise attenuation in medical images is proposed. Its operation takes place through the application of an adapted genetic algorithm. The results of experiments show that the proposed approach works best in suppressing artifacts and the preservation of the structure compared with several existing methods.Subject Classification: 68U10Keywords: DICOMGenetic AlgorithmNoise Attenuation
Extraordinary Remaining Potential in the Pre-Salt of Santos Basin

The Pre-Salt of the Santos and Campos Basins in Southeastern Brazil is a remarkable, world-class petroleum province.First discovery took place in 2006 and first production in 2008.Ten years in a row and production by the end of 2018 was 1,9 MMboepd, being 1,5 MMbopd from 85 wells only.The landmark of 1 MMbopd was achieved after 8 years of production.Cumulative production is over 2 billion barrels.Forty wells produced more than 20 Mbopd, with peaks of 42-45 Mbopd.One field alone, Tupi Field, produces 897 Mbopd.More than half (56%) of the Brazilian production of hydrocarbons nowadays comes from the Pre-Salt.Three fields (Tupi, Búzios and Iara Complex) can be classified as super-giants while four others are giant fields (Parque das Baleias, Mero, Sapinhoá, Lapa).Known discoveries are candidates for the super-giant (Carcará) and giant (Sagitário) status.Recently leased blocks contain very large prospects that are such candidates as well.
Climate variability and change over Uzbekistan – an analysis based on high resolution CHELSA data

Climate change and variability represent a major risk for the Central Asian economies, particularly for the agriculture and energy sectors. A detailed analysis of recent climatic changes is required in order to assess their impact. However, the observational network in Central Asia has significantly degraded after the dissolution of the Soviet Union, especially in the vulnerable high mountain regions (Unger-Shayesteh et al. 2013). Currently, the hydro-meteorological network of Uzhydromet is being modernized. Gridded climate data with high resolution represent a great opportunity to overcome these data related challenges, since they are freely available to all scientists and practitioners and enable a cross-border monitoring of climatic conditions. Here the authors introduce the CHELSA (“Climatologies at high resolution for the earth's land surface areas“) data set, which provides monthly data of precipitation and temperature for the entire globe at a resolution of 1x1 km for the period 1979-2013. The data set is evaluated against 20 stations across Uzbekistan. The authors show that CHELSA agrees in most cases with station observations, which is manifested in small deviations of mean seasonal temperature and precipitation sums. Further, temperature and precipitation trends as well as the relationship with large-scale atmospheric circulation modes, such as the El Nino Southern Oscillation and the North Atlantic Oscillation, are well represented by the CHELSA data set. The authors conclude, that CHELSA is a reasonable basis for the investigation of climate variability and change in Central Asia.
Instructional Design in Virtual Reality Environments: The case of Palestinian HEIs

The core aim of the paper is to present the results of facing the challenge of instructional design in a Virtual Reality environment in the context of the capacity building Erasmus+ KA2 project “Virtual Reality as an Innovative and Immersive Tool for HEIs in Palestine (TESLA)”. The instructional design concerns the development of educational courses based on Virtual Worlds technology for use in Palestinian Higher Educational Institutions (HEIs). Given that existing instructional design models were not sufficient for the above task, the team of the University of Patras (UPAT) developed the project's Instructional Design Model, as an integration of existing models, supporting the technological design with an efficient pedagogical well-structured framework.
Effects of Latency in Visual Feedback on Human Performance of Path-Steering Tasks

Effects of latency in a visual feedback system during path-steering tasks were examined. Input movements were recorded in high speed and projected with controlled latency, while a user was required to drag an object through a tunnel path. We tested the system with 10 people and analyzed the duration based on the steering law and revealed that the latency of visual feedback beyond 64.3 ms was associated with reductions in user performance, which provides suggestions in developing user-oriented interface designs.
Style Your Face Morph and Improve Your Face Morphing Attack Detector

A morphed face image is a synthetically created image that looks so similar to the faces of two subjects that both can use it for verification against a biometric verification system. It can be easily created by aligning and blending face images of the two subjects. In this paper, we propose a style transfer based method that improves the quality of morphed face images. It counters the image degeneration during the creation of morphed face images caused by blending. We analyze different state of the art face morphing attack detection systems regarding their performance against our improved morphed face images and other methods that improve the image quality. All detection systems perform significantly worse, when first confronted with our improved morphed face images. Most of them can be enhanced by adding our quality improved morphs to the training data, which further improves the robustness against other means of quality improvement.
Development of Concentration Future Musician-Teacher in the Class of Solo Musical and Instrumental Performance

The article examines the basic characteristics of concentration as most important property of attention of future teachers-musicians and the methods which help its effective formation during piano lessons. Studying of scientific approaches to this concept of a context of physiology, the general psychology, pedagogy showed that concentration acts as the most important psychological condition of deeper understanding of objects and the phenomena, optimum course of cognitive and practical activities of the person, especially in the course of the learning and creativity. A person activity, enthusiasm and continuing interest in a concentration subject help increase of attention concentration. The success of development of this property of attention in many respects depends on application by the teacher of special receptions for the organization of activity with an object, allocations in new aspects, their analysis and comparison, search for new properties of an object, ways and opportunities of its application or improvement. Special focus is placed on characteristics of the conceptual ideas of music pedagogy for development of attention concentration of students. The carried-out analysis of positions of authoritative representatives of performing art and music pedagogy showed that concentration is a starting point of musical and performing creativity and an important condition of efficiency of pianist work. Summarize experience of outstanding representatives of piano performance and theatrical pedagogy allowed to suggest the effective approaches to development of concentration as the significant quality in no small measure influencing disclosure of creative potential of future musician-teacher and defining a possibility of his productive self-realization in an instrumental and performing activity. The author comes to a conclusion that the ability to concentrate can be developed under the leadership of the teacher during training in a class, on rehearsals and also in homework when performing specially organized exercises. The persistence in mastering the concentration equipment by performance of specially organized exercises will allow the musician to operate consciously process of attention, it is essential to increase concentration time on the solution of a creative task.
IMPLEMENTASI ALGORITMA REGRESI LINEAR SEDERHANA DALAM MEMPREDIKSI BESARAN PENDAPATAN DAERAH (STUDI KASUS: DINAS PENDAPATAN KAB. DELI SERDANG)

Data Mining is an information discovery by extracting information patterns that contain trend searches in a very large amount of data and assist the process of storing data in making a decision in the future. In determining the pattern classification techniques do to collect records (Training set). Regional income is generally derived from local taxes and levies, local taxes are one source of funding for the region on the national average has not been able to make a large contribution to the formation of local revenue. By utilizing Regional Revenue data, it can produce forecasting and predictions of Regional Revenue income in the future to match the reality / reality so that the planned RAPBD can run smoothly. Simple Linear Regression or often abbreviated as SLR (Simple Linear Regression) is one of the statistical methods used in production to make predictions or predictions about the characteristics of quality and quantity to describe the processes associated with data processing for the acquisition of regional income. So that in the testing phase with visual basic net can help in processing valid Regional Revenue Amount data. Keywords: Data Mining, Local Revenue, Simple Linear Regression Algorithm, Visual Basic net 2008
Security-Aware Data Allocation in Multicloud Scenarios

When moving large and heterogeneous data collections to the cloud, a key requirement concerns the selection of the most suitable (set of) cloud service(s) for outsourcing. Not only can different resources have different characteristics and requirements, but different cloud providers can also offer different services and security guarantees, and can have different costs. Selecting a single service for outsourcing an entire data collection can result in a non-optimal solution, as a single service satisfying, at reasonable costs, all the requirements specified by the data owner might not exist. Selecting a set of services could instead ensure the satisfaction of the requirements, possibly with economic advantages. In this article, we address this problem and present a flexible and expressive, yet simple model for supporting data owners in identifying a proper allocation of their resources to a set of cloud services. Our model allows data owners to specify in an easy and intuitive way protection requirements operating at the granularity level of single resource (or class thereof), and representing the minimum security guarantees that a cloud service must offer to store resources. Resources can be outsourced in plaintext or encrypted form, depending on their requirements and on what is the most convenient allocation. Data owners can then also specify global allocation requirements that apply to the overall allocation, to reduce the burden on their side and to avoid excessive fragmentation of the resource collection. We solve the problem of finding an allocation that satisfies both the protection and the global allocation requirements, while minimizing economic costs, by formulating it as a binary programming problem, thus allowing the use of existing techniques for its efficient solution.
Phenotyping UK Electronic Health Records from 15 Million Individuals for Precision Medicine: The CALIBER Resource.

Electronic health records (EHR) are increasingly being used for observational research at scale. In the UK, we have established the CALIBER research resource which utilizes national primary and hospital EHR data sources and enables researchers to create and validate longitudinal disease phenotypes at scale. In this work, we will describe the core components of the resource and provide results from three exemplar research studies on high-resolution epidemiology, disease risk prediction and subtype discovery which demonstrate both the opportunities and challenges of using EHR for research.
A Mondrian-based Utility Optimization Model for Anonymization

Anonymization is a privacy-preserving approach facilitating to protect the identities of data subjects. Besides privacy, anonymization presents a data utility that is very important for data analytics. The accuracy of a data analytics model, which is created on anonymous data, depends on the utility provided by anonymization. While there exist some factors directly affecting data utility such as privacy level, anonymization operators, anonymization algorithm, etc. Recent studies presented that outliers are another factor that affects data utility too. In the anonymization domain, outliers are accepted as “a group of data that decreases total data utility”. In this paper, in order to maximize the data utility, a new data utility model is proposed and applied to Mondrian for the first time. A general outlier-based utility evaluation function is also introduced to measure this utility for the first time. Experimental results have shown that the proposed model improves data utility and presents higher utility than Mondrian. The proposed function might be also used as a reliable tool for outlier-based utility-aware anonymization models.
Selecting and Sliding Hidden Objects in 3D Desktop Environments

Selecting and positioning objects in 3D space are fundamental tasks in 3D user interfaces. We present two new techniques to improve 3D selection and positioning. We first augment 3D user interfaces with a new technique that enables users to select objects that are hidden from the current viewpoint. This layer-based technique for selecting hidden objects works for arbitrary objects and scenes. We then also extend a mouse-based sliding technique to work even if the manipulated object is hidden behind other objects, by making the manipulated object always fully visible through a transparency mask during drag-and-drop positioning. Our user study shows that with the new techniques, users can easily select hidden objects and that sliding with transparency performs faster than the common 3D widgets technique.
Clustering of Earthquake Prone Areas in Indonesia Using K-Medoids Algorithm

Located right above the ring of fire makes Indonesia prone to natural disasters, especially earthquakes. With the number of earthquakes that have occurred, disaster mitigation is very much needed. The use of data mining methods will certainly help in disaster mitigation. One method that can be used is clustering. The clustering algorithm used in this study is k-Medoids, and comparison with the k-means algorithm is also carried out. The data used are earthquake data from all regions in Indonesia during 2014-2018 that were recorded by the United State Geological Survey (USGS). The results obtained showed that k-medoids giving better silhouette results and computational time than k-means. For the k-medoids cluster results, the highest value of silhouette was 0.4574067 with k = 6. The analysis of each cluster is presented in this paper. Keywords: clustering,data mining, earthquake, k-medoid.
Comparison of spatial and temporal interaction techniques for 3D audio trajectory authoring

With the popularity of immersive media, developing usable tools for content development is important for the production process. In the context of 3D audio production, user interfaces for authoring and editing 3D audio trajectories enable content developers, composers, practitioners, and recording and mixing engineers to define how audio sources travel in time. However, common interaction techniques in 3D audio production tools can make the workflow of this task tedious and difficult to accomplish. This study investigates this problem by classifying the atomic tasks (spatially and temporally) of a general composite task of authoring 3D audio trajectories and then evaluating different interaction techniques across these tasks. Common graphical user interfaces were compared with input devices having varying degrees-of-freedom for spatial atomic tasks in order to investigate the effect of direct manipulation and integrality of interaction techniques. Continuous and discrete interaction techniques were compared for temporal tasks in order to investigate the effect of direct manipulation. Results suggest that interaction techniques with high degrees of integrality and direct manipulation reduce task completion time compared to standard GUI techniques. The design of temporal tasks can create a visual bias, and discrete-time controls can be a suitable method for traversing a small number of control points. These results and further observations provide directions on the study of interaction technique design for 3D audio tools, which in turn should improve workflows of 3D audio content creation.