Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing an active heterogeneous database system

The heterogeneous environment is emerging in current information systems. Within this situation, it is desirable to maintain consistency across databases, i.e., to ensure that they do not contradict each other with respect to the existence or value of a real-world entity. Active rules have been widely used in modern database systems. Active databases contain the capability to automatically monitor the conditions which are defined over the database state and the ability to take an action when the state of the underlying database changes. They have provided a great contribution to the maintenance of data integrity and data consistency in database systems. This paper investigates how to integrate active rules into a heterogeneous database system environment.
The Impact of Colonisation on the Ability to Make a Meaning of 'Black' South African Contemporary Dance in the 21st Century

This paper will explore two examples of African contemporary dance, including the 2014 Uhambo 1 production in Athlone Cape Town and Four Seasons, choreographed by Gregory Maqoma and performed by the Vuyani Dance Theatre at Artscape Theatre Cape Town in 2014. Hence, it also argues that one critical way of supporting audiences to resist past and present forms of oppression is for the creation of meaning (value) through interdisciplinary choreography and post-performance discussions with audience members which will allow Black South Africans to begin to reclaim their ability to reflect on the meaning of contemporary African dance forms, and in turn, to reflect and act upon confidently, through their own
Cloud Signature Creator An application to establish cloud-computing application artifacts

As the percentage of computer users that utilize cloud-computing services grows, more potential evidence for state and local law enforcement investigators is being stored with these cloud services and not on a local computer’s hard drive. To address this problem, this project created a tool called Cloud Signature Creator as a solution that allows an investigator to locate potential areas of a computer’s file system that contains evidence useful to their investigation. The Cloud Signature Creator solution leverages existing technologies and implements a new software application that provides the end user with a listing of files and locations that might indicate a cloud service was utilized on a suspect computer.
Market Power and Monetary Policy

In this paper we explore the link between monetary policy and market power. We start by establishing several facts on market power in UK markets using micro data. First, while no clear trend emerges for market concentration, market power measured by markups estimated at the firm level have clearly increased in recent years, with the rise being reasonably broad-based across sectors. Second, we show that the increase is heavily concentrated in the upper tail of the distribution — companies whose mark-ups are in, say, the top quartile. Third, internationally-oriented firms are the driving force behind the rise in markups. Fourth, following Diez et al (2018), we find some reduced-form evidence of a non-monotonic relation between markups and investment at the firm level, with high levels of markups being associated with lower investment. Having established these facts, we show that the Phillips curve becomes steeper in the textbook New Keynesian model when firms tend to have more market power, reducing the sacrifice ratio for monetary policy. As inflation becomes less costly in an economy with high market power, however, the optimal targeting rule for monetary policy also changes. A rise in both the trend and volatility of mark-ups may lead to a significant rise in inflation variability. But a secular rise in mark-ups by itself improves monetary policy’s ability to stabilise inflation without inducing large movements in output.
RETROSPECTIVE RATING: EXCESS LOSS FACTORS

This paper explains how Excess Loss Factors (ELFs) are computed. It is organized so the essential elements of the computation are described first, then the detailed origins of these elements are added. The detail may be found in the many spreadsheets used in the production of ELFs. The writer has attempted to show how each fits into the structure and leads to the final values. The calculations are quite technical; to understand the whole, it may be necessary to trace the numbers through the spreadsheets using the text as a guide. The procedure for computing ELFs has changed since it was last documented, the most signijkant revision occurring in 1986. In describing the parts that have changed, the author has supplied justtfications, or at least rationales, for the particular changes.
Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach

Approximately one-third of the food produced globally is spoiled or wasted in the food supply chain (FSC). Essentially, it is lost before it even reaches the end consumer. Conventional methods of food waste tracking relying on paper-based logs to collect and analyse the data are costly, laborious, and time-consuming. Hence, an automated and real-time system based on the Internet of Things (IoT) concepts is proposed to measure the overall amount of waste as well as the reasons for waste generation in real-time within the potato processing industry, by using modern image processing and load cell technologies. The images captured through a specially positioned camera are processed to identify the damaged, unusable potatoes, and a digital load cell is used to measure their weight. Subsequently, a deep learning architecture, specifically the Convolutional Neural Network (CNN), is utilised to determine a potential reason for the potato waste generation. An accuracy of 99.79% was achieved using a small set of samples during the training test. We were successful enough to achieve a training accuracy of 94.06%, a validation accuracy of 85%, and a test accuracy of 83.3% after parameter tuning. This still represents a significant improvement over manual monitoring and extraction of waste within a potato processing line. In addition, the real-time data generated by this system help actors in the production, transportation, and processing of potatoes to determine various causes of waste generation and aid in the implementation of corrective actions.
Naira devaluation and trade balance in Nigeria

This paper examines the effects of Naira devaluation on trade balance in Nigeria. Annual time’s series data over the period ranging from 1986 to 2017 was used with Engle-Granger cointegration test used to test the existence of a long run relationship. The results of the estimation show that Naira devaluation had no significant influence on change in trade balance in the long-run in Nigeria in the periods under study. The result also suggests that Naira devaluation exerts no significant impact on trade balance in Nigeria over the study periods. The paper recommend that Nigerian currency should be allowed to depreciate freely through market forces and efficient money market system, official devaluation should be discouraged.
DETECTING USER'S BROWSING BEHAVIOR USING FREQUENT PAGES CONSTELLATION ALGORITHM

The World Wide Web provides colossal volume of data to the internet users and grows at a rapid pace every day. The server creates log files regarding the page, IP address of the user, agent, operating system and time stamp and this data is mined to extract useful information using web usage mining. The primary objective of this paper is to find the browsing behavior pattern of the users from the log files using a novel algorithm named "Frequent Pages Constellation Algorithm". The FPC Algorithm groups the frequent pages browsed by the users and the results produced is utilized for personalization, forecast market vogues, enhances marketing strategies, improve website page positioning, site restricting etc. The experimental results showcased that the proposed FPC algorithm generates very less candidates, takes very less
HLJLTIBEAM ACTIVEPHASEDARRAYSYSTEMCONFIGURATIONSFOR COMMUNICATIONS SATELLITES

Thispaperpresentsarchitectures for activephasedarraysformultiplescanning, hopping,andreconfigurable shapedbeamsfor satelliteapplications.A comparison of the differentarchitecturesisdiscussed.An experimentalmodelwhichincorporates active monolithicmicrowaveintegrated circuit (MMIC)componentsforbeamcontrolisdescribed.Thetechnologyforthismodelis directlyapplicable to thevariousarchitecturespresented.
MeTA: A Modern C++ Data Sciences Toolkit

MeTA is developed to unite machine learning and information retrieval algorithms in one easy-to-use toolkit. Its focus on indexing allows it to perform well on large datasets and support online classication. MeTA’s liberal open source license encourages contributions, and its extensive online documentation and tutorials make this process straightforward. We perform experiments and show MeTA’s performance is competitive with existing software in both domains.
Automatic Testing of Software in University Courses

The appropriate testing of software is an important issue for industry, and one where many enterprises fail. This can in part be blamed on inadequate teaching in the undergraduate curriculum. This paper examines the causes of inadequate teaching of software testing in Universities and then goes on to propose solutions. A tool created by the author called E-AutoTest is introduced that can run, mark, Email feedback to students, and provide collated reports. Hundreds of student programs per hour can be processed using E-AutoTest. This removes the labour and time barrier that is a significant issue in the evaluation of student programs.
Discontinuous operation of geothermal heat exchangers

Ground source heat pump (GSHP) systems for HVAC have aroused more and more interest in China in recent years because of their higher energy efficiency compared with conventional systems. The design and performance simulation of the geothermal heat exchangers is vital to the success of this technology. In GSHP systems, the load of the geothermal heat exchanger varies greatly and is usually discontinuous even during a heating or cooling season. This paper outlines a heat transfer model for geothermal heat exchangers. The model was used to study the influence of the discontinuous operation of the heat pumps on the performance of the geothermal heat exchangers. A simple and practical approach is presented for sizing the geothermal heat exchangers. 
Dynamic Pricing in a Multi-Period Newsvendor Under Stochastic Price-Dependent Demand

The faster growth of technology stipulates the rapid development of new products; with the spread of new technologies old ones are outdated and their market demand declines sharply. The combined impact of demand uncertainty and short life-cycles complicate ordering and pricing decision of retailers that leads to a decrease in the profit. This study deals with the joint inventory and dynamic pricing policy for such products considering stochastic price-dependent demand. The aim is to develop a discount policy that enables the retailer to order more at the start of the selling season thus increase the profit and market share of the retailer. A multi-period newsvendor model is developed under the distribution-free approach and the optimal stocking quantities, unit selling price, and the discount percentage are obtained. The results show that the proposed discount policy increases the expected profit of the system. Additionally, the stocking quantity and the unit selling price also increases in the proposed discount policy. The robustness of the proposed model is illustrated with numerical examples and sensitivity analysis. Managerial insights are given to extract significant insights for the newsvendor model with discount policy.
Access Rights in Enterprise Full-text Search

One of the toughest problems to solve when deploying an enterprise-wide full-text search system is to handle the access rights of the documents and intranet web pages correctly and effectively. Post-processing the results of general-purpose full-text search engine (filtering out the documents inaccessible to the user who sent the query) can be an expensive operation, especially in large collections of documents. We discuss various approaches to this problem and propose a novel method which employs virtual tokens for encoding the access rights directly into the search index. We then evaluate this approach in an intranet system with several millions of documents and a complex set of access rights and access rules.
Modeling and rendering the invisibles and the impossibles from single images: a human-computer interaction approach

A human being has a remarkable ability to make 3D connection on seeing 2D images. For example, although transparent objects are invisible to a large extent, we can mentally infer the 3D solid almost instantly and effortlessly. This ability, however, sometimes leads to interesting problems. M. C. Escher was a renowned artist who specialized in embedding impossible figures into architectural drawings. Impossible figure is a special kind of drawing consisting of multiple geometrically possible units connected by linear structures. When the figure is viewed as a whole, structural inconsistencies arise and confuse our visual perception. The theme of this thesis consists of an appearance-based computational framework for modeling the "invisibles" and the "impossibles", that is transparent objects and impossible figures. We applied our appearance-based model in computer graphics, allowing for the first time the rendering of transparent objects in a new background scene, as well as the rendering of impossible figures at high frame rates, both without any tedious 3D modeling. The key in our approach lies in bringing the user into the modeling loop, i.e., human-computer interaction (HCI). Human-computer interaction, which can be understood as the supplying of a small amount of hints to help automatic computer algorithm to solve difficult problems, has recently gained a lot of attention in research in computer vision and interactive techniques. The main issue lies in improving computer's performance by user interaction using a simple interface. This thesis presents a HCI approach to model and render transparent objects (the invisibles) and impossible figures (the impossibles) which are traditionally very difficult in computer vision and graphics. In this thesis, we exploit our remarkable human visual system to provide prior knowledge, and propose an HCI approach to transfer such prior information in the form of a few simple hints via an easy and intuitive user interface. The computer algorithm then automatically performs the rest of the processing. We first transform the problem of transparent layer extraction into a soft-segmentation problem, where simple user's hints are available in the form of rough strokes drawn on the image. Then, with the aid of a human user who can easily recognize transparent objects given a single photo, we derive a practical and interactive approach to solve the problem of matting and compositing of transparent and refractive objects. Traditionally, these tasks can only be achieved using multiple images or 3D models. Finally, we present two approaches to model and render impossible figures, which is a long-standing problem in computer vision and computer graphics.
A Wind Speed Estimation Method for Quadcopter using Artificial Neural Network

—This paper presents an approach for quadcopter that estimates the wind speed in the outdoor airflow environments based on artificial neural network. Wind speed estimation is an essential for quadcopter for the odor source localization in outdoor airflow environments. We present a novel method of wind speed estimation by using an artificial neural network. To accomplish the mission we use MATLAB software. According to the estimation result obtained from both backpropagation and exact radial base function artificial neural network the wind speed estimation result is promising and both artificial neural networks can be used for the wind speed estimation of the quadcopter.
Social Media Roadmaps: Exploring the futures triggered by social media

Social media refers to a combination of three elements: content, user communities and Web 2.0 technologies. This foresight report presents six roadmaps of the anticipated developments of social media in three themes: society, companies, and local environment. One of the roadmaps, the meta-roadmap, is the synthesis of them all. The society sub-roadmap explores societal participation through communities. There are three sub-roadmaps relating to companies: interacting with companies through communities, social media in work environment, and social media enhanced shopping. The local environment sub-roadmap looks at social media in local environment. The roadmapping process was carried out through two workshops at VTT. The results of the report are crystallized into five main development lines triggered by social media. First development line is transparency referring to its increasing role in society, both with positive and negative consequences. The second development line is the rise of ubiquitous participatory communication model. This refers to an increase of two-directional and community-based interactivity in every field, where it has some added value. The third development is reflexive empowerment. This refers to the role of social media as an enabler of grass-root community collaboration. The fourth development line is the duality personalization/fragmentation vs. mass effects/integration. Personalization /fragmentation emphasises the tailoring of the web services and content. This development is counterweighted by mass effects/integration, like the formation of super-nodes in the web. The fifth development line is the new relations of physical and virtual worlds. This development line highlights the idea that practices induced by social media, e.g. communication, participation, co-creation, feedback and rating, will get more common in daily environment, and that virtual and physical worlds will be more and more interlinked.
JSON Web Signature (JWS) draft-ietf-jose-json-web-signature-11

JSON Web Signature (JWS) is a means of representing content secured with digital signatures or Message Authentication Codes (MACs) using JavaScript Object Notation (JSON) based data structures. Cryptographic algorithms and identifiers for use with this specification are described in the separate JSON Web Algorithms (JWA) specification. Related encryption capabilities are described in the separate JSON Web Encryption (JWE) specification.
Field Drilling Data Cleaning and Preparation for Data Analytics Applications

Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different wells drilled in an area of 2 x 4 miles. The data was collected in the same rig and contains over 12 million electronic drilling recorder data points, driller’s activity logs and well profiles. The main focus is to propose a detailed workflow to clean and process real drilling data. Once cleaned, the data can be fed into data analytics platforms and machine learning models to efficiently analyze trends and plan future well more efficiently. This roadmap will serve as a basis for drilling optimization. The objective of this work is to detail the various steps needed to prepare field drilling data for business analysis, as well discuss about data analytics and machine learning application in drilling operations. The results to be presented are the detailed workflow and description of the data preparation steps, an example analysis of the drilling data and an example application of a machine learning model in drilling.
Protecting Java Code Via Code Obfuscation

The language Java was designed to be compiled into a platform independent bytecode format. Much of the information contained in the source code remains in the bytecode, thus decompilation is easy. We will examine how code obfuscation can help to protect Java bytecodes. The objective of our work is to develop and use obfuscation techniques that produce obfuscated bytecode that is very hard to reverse engineer while at the same time not significantly degrading performance.
Internet of things based multi-sensor patient fall detection system

Accidental falls of patients cannot be completely prevented. However, timely fall detection can help prevent further complications such as blood loss and unconsciousness. In this study, the authors present a cost-effective integrated system designed to remotely detect patient falls in hospitals in addition to classifying non-fall motions into activities of daily living. The proposed system is a wearable device that consists of a camera, gyroscope, and accelerometer that is interfaced with a credit card-sized single board microcomputer. The information received from the camera is used in a visual-based classifier and the sensor data is analysed using the k-Nearest Neighbour and Naïve Bayes' classifiers. Once a fall is detected, an attendant at the hospital is informed. Experimental results showed that the accuracy of the device in classifying fall versus non-fall activity is 95%. Other requirements and specifications are discussed in greater detail.
Introduction to SAS ® Hash Objects

The SAS hash object is an incredibly powerful technique for integrating data from two or more datasets based on a common key. The current work describes the basic methodology for defining, populating, and utilizing a hash object to perform lookups within the DATA step and provides examples of situations in which the performance of SAS programs is improved by their use. Common problems encountered when using hash objects are explained, and tools and techniques for optimizing hash objects within your SAS program are demonstrated.
INDOOR POSITIONING BASED-ON IMAGES AIDED BY ARTIFICIAL NEURAL NETWORKS

Abstract. Indoor positioning has attracted much attention in recent years due to the trend of Internet of Things (IoT), which is capable of providing numerous applications such as personal tracking, vehicle locator, and Location-Based Service (LBS). To put LBS into practice, positioning and navigation are one of the necessary techniques. Using the smartphone to process indoor positioning also become more and more usual. The most common algorithm in the inertial navigation system is Pedestrian Dead Reckoning (PDR), utilizing sensors built-in the smartphones to conquer the strait of GNSS-denied environment. However, for the purpose of eliminating the error accumulated with time, PDR combining with other algorithms, for instance, updating some geospatial information steadily is a better way to solve this problem. Therefore, this research proposes the imaged based aided algorithm. Moreover, in this study, a novel Artificial Neural Networks (ANN) embedded the system is proposed. The self-designed georeferenced markers and the indoor floor plan will be produced by an Indoor Mobile Mapping System (IMMS) in advance. This research proposed using Cascade-Correlation neural Network (CCN) to estimate the distance between the marker and the smartphone camera. The accuracy using this method can achieve to 0.27 meter. As if at least three coordinates and the distance can be obtained simultaneously, the position of the user can be calculated by the trilateration method. From the experiment, the accuracy of the positioning is about 0.5 meter. This way seems to have the high potential to bring into play on the real-time indoor positioning.