Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF BOARD COMPOSITION ON VOLUNTARY DISCLOSURE OF EMPLOYEE INFORMATION OF LISTED COMPANIES IN NIGERIA

The study investigated the board composition as a determinant of voluntary disclosure of employee-related information in the financial statement of quoted non-financial firms in Nigeria. Employee information measured by the number of sentences used to report employee-related information is the dependent variable. A panel data covering 6-year time series from 50 firms were obtained from the financial statements of the quoted firms in Nigeria. The Fixed and Random Effect models were used for data analyses. The Hausman test was employed to determine which of the models is more appropriate in each of the regression analyses. The results showed that board composition, has significant positive effects on employee information reported on the financial statement in Nigeria. Following that board composition has 10% explanatory power on employee-related information disclosure, the study conclude that Ullmans’s framework of strategic posture is a poor determinant of voluntary disclosure of employee-related information in Nigeria.
The draft national tourism strategy in the context of destination image recovery in Zimbabwe

This paper sought to carry out a comparative assessment of the Zimbabwe Draft National Tourism Sector Strategy (DNTSS) while adding insights drawn from desk research and a doctoral study whose aim was the development of a destination image (DI) recovery model for enhancing performance of the tourism sector in Zimbabwe. The DNTSS and the doctoral study coalesced on this overarching objective of achieving DI recovery so as to enhance the economic performance of Zimbabwe’s tourism sector. While data for the two researches was collected almost simultaneously, the key findings were not the same. The doctoral research, using a mixed methodology, revealed that a poor image contributed significantly to the decline in tourism’s economic performance while the DNTSS was silent on the cause. Data was collected from various locations from a sample of 311 comprising international tourists, tourism and hospitality service providers and key informants. A structured and semi-structured questionnaire as well as a semi-structured interview guide were used respectively to collect data for the doctoral study while stakeholder consultations were used for the DNTSS. The doctoral study revealed that there was need for Zimbabwe to implement political and economic strategies and to analyze the tourism value chain with a view to have competitive prices and turn around DI. The study recommended that in the DNTSS, the market could be divided into distinct components such as core markets and investment markets. It was also crucial to invest in tourism research and grow arrivals from markets with under-tapped potential such as African source markets and parts of Asia/Pacific. It was felt that the views of the villagers and international tourists could have been incorporated in the DNTSS. The study’s implications were that political strategies needed to be complemented by other strategies in order for the national tourism strategy to be more effective and also that the principle of inclusivity and diversity was central in DI recovery.
Automatic Construction of the Polish Nominal Lexicon for the OpenCyc Ontology

This paper describes the automatic construction of the Polish nominal lexicon for the OpenCyc ontology. The ontology is shipped with the English lexicon, thus the goal is achieved by translating the English words corresponding to the OpenCyc symbols into Polish. As a result the mapping between the ontology symbols and the Polish words is created. The developed algorithm utilizes semantic properties of the ontology, as well as a machine readable English-Polish dictionary („Wielki Multimedialny Słownik angielsko-polski polsko-angielski Oxford/PWN”). The algorithm is heuristics-based and purely symbolic.
Usual Duplicate Detection Using Progressive Algorithms with Improving Efficiency

Duplicate detection is the process of classifying numerous representations of same real world entities. Presently, these methods made essential to route ever higher datasets in constantly squatter period and sustaining the eminence of a dataset befits progressively problematic. Progressive duplicate detection algorithms significantly intensify the efficiency of discovering replicas if the execution time is inadequate. Exploiting the expansion of the overall process within the time available by reporting results in much prior than previous methodologies. Here, Widespread tests display that progressive algorithms can double the efficiency over time of traditional duplicate detection and ominously progress upon
Influence of Intellectual Capital Information Disclosure on Dividend Policy : Case of KSE Listed Non-Financial Firms of Pakistan

This research study influence the impact of ICD on dividend policy. ICD is estimated by using intellectual capital index and for dividend policy, DPO is used as proxy. ICD is significantly positively influence the DPO which clearly demonstrate that higher intellectual capital disclosure leads the higher DPO. These findings also depicts that reducing trend of information asymmetry became the cause of high level of disclosure (Christien & Farooq, 2015) the outcomes of this study are similar to prior studies which also conclude that higher ICD leads to higher DPO. It has also been claimed that high DPO is a result of proper information environment that bound the insiders from exploiting firm’s resources. (Nilsen & Farooq, 2015). Findings of this work are also consistent with preceding literature which reflect that the lower level of agency issues leads to increase in DPO. (Porta & Vishny, 2000) (Li&Zhao, 2008). Firm size insignificantly impacts on the policy of dividend in all three models. These results are same as (Shehzad Khan, 2015) which also specify that there is no impact of firm size on dividend policy and this is contrary to (Nilsen & Farooq, 2015) results. Return on asset ratio use to evaluate the profitability of firm and assumes that, dividend policy significantly affected by the Return on asset (ROA). Results are consistent with prior studies which also determined that Return on asset has significant impact on DPO in the non-financial PSE listed firms. Moreover, these findings are same as (Ahmed, 2009) and (Paul Healy, 2001). Mostly, it is considered that the firm that have higher liquidity will pay the lower payment of dividends but the findings are with (Friend and Puckitt,1964), which Sunya RamzanInfluence of Intellectual Capital Information Disclosure on Dividend Policy: Case of KSE Listed Non-Financial Firms of Pakistan EUROPEAN ACADEMIC RESEARCH Vol. VI, Issue 9 / December 2018 5243 show that there is no any relationship exist among liquidity and DPO. Key findings associated to Earning ratio (EPS) and DPO are observed which described that the both increasing and decreasing trend of Earnings have insignificant impact on DPO. It is also determined that the firms that the distribution of dividend payments is mandatory regardless of firm’s earning. The reason behind this may be strict and rigid rules, policies, and guideline of PSE and may be for the purpose of attaining good image in the market.
Three-dimensional Irregular Tree Modeling For Special Virtual Environments

Computer synthesized three-dimensional (3D) tree models often appear in virtual environments of 3D movies and games. They act as decorations or functional components, making the virtual worlds more realistic. Although regular tree shapes are commonplace, irregular tree shapes with highly crooked branches are also needed in some special virtual scenes. This paper presents a procedural method for 3D irregular trees modeling. The whole tree structure is generated in a 3D weighted graph and tree branches are least-cost paths connecting selected points. The scale of branch irregularity is controlled via a mechanism of setting the edge weights. The resulting models have realistic tree characteristics, and also demonstrate the desired features of irregularity and heterogeneity.
THE POTENTIAL

the Department of Institutions, could be left up to the state-wide organization to decide. It does seem, friends and coworkers, that with the great increase of mental cases each year, and the present state of our civilization, this mental field is being overlooked, and we should not postpone any longer trying to do more in the preventive field, if ever we expect to improve our race to the point where we can say our people in California surpass those of any other state or country.
Modular Reconfigurable Robotics

This article reviews the current state of the art in the development of modular reconfigurable robot (MRR) systems and suggests promising future research directions. A wide variety of MRR systems have been presented to date, and these robots promise to be versatile, robust, and low cost compared with other conventional robot systems. MRR systems thus have the potential to outperform traditional systems with a fixed morphology when carrying out tasks that require a high level of flexibility. We begin by introducing the taxonomy of MRRs based on their hardware architecture. We then examine recent progress in the hardware and the software technologies for MRRs, along with remaining technical issues. We conclude with a discussion of open challenges and future research directions.
Assessment of Glycaemic Effect of Benincasa hispida Aqueous Extract in Streptozotocin Diabetic Rats

Benincasa hispida, Bh (winter melon) is a good source of natural sugars, amino acids, organic acids, mineral elements and vitamins. This study was designed to explore the glycaemic effect of Bh aqueous extract in diabetic rats. Male Sprague-Dawley rats (250350g) were induced to become diabetic with streptozotocin (STZ, 45mg/kg intraperitoneal). The Bh aqueous extracts of 100, 250 and 500 mg/kg and metformin were administered through oral gavage to determine their effects on fasting blood glucose (FBG) and oral glucose tolerance test. The dose of 250 mg/kg was found to be the most effective dose and it decreased blood glucose level (BGL) by 11.8% in normal healthy rats after 6H of administration. The same dose also caused a marked reduction in BGL of 33.1% in normal rats, 38.7% in severe diabetic rats and 64.7% in mildly diabetic rats in OGTT after 3H of oral administration of the extract. The changes of BGL were not significant compared to normal rats. It shows that the extract is diabetic-friendly since the blood glucose levels were stable throughout the study period. These trends of BGL reduction show that Bh aqueous extract is diabetic friendly.
QA with Wiki : improving information retrieval and machine comprehension

The open-domain question answering task has recently been addressed using (a) structured data in knowledge bases, (b) unstructured data such as websites and online encyclopedias, and (c) a combination of both. An example of (b) is the DrQA system by Chen et al. [2017], where open-domain questions are answered through (a) finding Wikipedia articles relevant to the question (document retrieval), and then (b) predicting an answer span within the retrieved articles (machine comprehension of text). This paper proposes to extend the DrQA implementation for both document retrieval and machine comprehension. For the former, we experiment with incorporating PageRank when computing the "document score" to assess relevance of a document to a query. For the latter, we experiment with manual features and with implementing the BERT architecture based on Devlin et al. [2018]. Our experiment with PageRank adjusted retriever suggests it is a promising direction to work on for performance improvement of the retriever module. BERT dramatically improves performance for machine comprehension; however, there is some evidence to suggest that performance gains aren’t as strong on answers with long spans.
Implementation of a Quad-Rotor Robot Based on SoPC

This work uses a SoPC platform as the flight controller of a quad-rotor aircraft. By CAD/CAM technologies the fuselage of the aircraft is built. The posture of the aircraft is stabilized according to the sensory data from a three-axle gyroscope and a tri-axia accelerometer. Additionally, a CMOS camera mounted on the bottom of the aircraft and an ultra-sonic sensor enables it to detect and track the object of interest on the ground while the aircraft is flying.
Fast Relational Learning using Bottom Clauses in Neural Networks

In this paper we introduce a method and algorithm for ILP learning using neural networks, by extending a neural-symbolic system called C-ILP (Garcez and Zaverucha, 1999). It is a neural-symbolic system that uses a background knowledge encoded as an initial propositional logic program to build a recurrent neural network and furthermore, uses examples to apply standard back-propagation learning. This integration inherited both parallel learning from neural networks and explanatory power from propositional logic. We will use the most specific clause of ILP systems that are based on Inverse Entailment, called Bottom Clause, to allow learning with neural networks. More precisely, Bottom Clauses will be generated for each training example and then, applied on an adapted version of C-ILP. Experimental comparisons with a traditional ILP system, Aleph, will be made using alzheimers benchmarks and the results show that our neural-symbolic approach for First-Order Logic has similar classification accuracy but it is considerably faster.
Hotel Characteristics and the Adoption of Demand Oriented Hotel Green Practices in Zimbabwe: A Regression

This paper determines the adoption levels of demand oriented green practices by hotels in Zimbabwe and then regresses these adoption levels with hotel characteristics. The study was prompted by the need to balance off the supply led skew in the country’s endeavours to project and promote itself as a green tourism destination and also to raise awareness of the need to accentuate the guest dimension in the greening of hotel operations as called for by various authors at a global scale. 50 out of the 124 registered hotels in the country constituted the study units. A quantitative research design was adopted for the research and the data was gathered through direct observation using an observation checklist. In terms of the first hypothesis, this study concluded that the adoption levels of demand oriented green practices by hotels in the country, was only 16% of those identified in existing literature. For the second hypothesis, the research rejects the alternate hypothesis and thus concludes that there is no significant relationship between hotel characteristics in the country and their adoption of demand oriented green practices. This conclusion is unexpected, surprising and a cause for concern for the country as one would expect higher quality hotels to be more sensitive to global trends on green issues. The study therefore suggests several ways by which hotels in the country edify the guest dimension in their greening efforts. Further research could be carried out to determine guest perceptions and attitudes towards these demand oriented green hotel practices.
A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks

In 2012, the National Non-Revenue Water assessment revealed that South Africa has 37% of non-revenue water. With the steadily growing demand for this scarce resource, the detection of leaks in pipe networks is becoming more important. Currently, in South Africa the primary method of detecting leaks is to install pressure management systems and monitoring minimum night time flows [1]. The pressure-flow deviation method, can be used to formulate an inverse analysis model based leak detection problem. This problem can then be solved using Artificial Neural Networks, Support Vector Machines and other optimization methods. With EPANET, different networks were tested to compare these methods to finding leaks, using an inverse analysis formulated problem. Four different numerical networks were modeled and tested, a simple single pipe network, a small agricultural site, a distribution network proposed and investigated by Poulakis et al. [2] and the simulated model of the experimental network that was designed and commissioned during the study in our laboratory. From the numerical investigation, it was found that the optimization methods struggled to find solutions for simple networks with infinite number of solutions for the problem. For more complex numerical networks, it was seen that the Support Vector machine and the Artificial Neural Networks trained to the averages of their respective data sets. Errors to ensure an accurate solution found by these algorithms were calculated as 2.6% for the numerical experimental network. The experimental network consisted of six possible leaking pipes, each having a length of 3m and a diameter of 10mm. Three leak cases were tested with diameters of 3mm and 2mm. Overall, the Support Vector machine could locate the leaking pipe with the best accuracy, while the minimizing of non-regularized error could calculate the size and location of the leak the most accurately. Multiple leak cases were measured with the experimental network. The Support Vector machine was tested on these measurements, where it was found that two of the three leak cases could be solved with relative accuracies. Sensor usage optimization was completed on i © University of Pretoria the measurements for the experimental network, where it was found that the leaks could be classified correctly with probabilities higher than 98% if only two sensors were used in the training of the SVM instead of all twelve. Overall this method of leak detection shows promise for certain applications in the future. With practical applications on water distribution, transportation, and agricultural networks. ii © University of Pretoria
Statistical image analysis of spotted arrays

STATISTICAL IMAGE ANALYSIS OF SPOTTED ARRAYS by Filippo Posta There is a lot of systematic and specific variability in microarray experiments, this variability affects measured gene expression levels, leading to unreliable gene profiling or an heavy load of extra experiment to statistically confirm the data observed in one experiment. The aim of this work is to systematically analyze, using statistics, the image derived from a cDNA microarray experiment to have a better understanding of this variability and thus a better confidence over the data obtained from an experiment. Using technologies available at the Center for Applied Genomics, Newark, New Jersey. Selected images derived from different type of microarray experiments have been analyzed in statistical fashion to find answers about the variability of biological data. Statistical methods such regression have been applied to the whole image, print-tips and single spots; leading to answers, confirmations and new ideas about issues regarding analysis and reliability of microarrays experiments. STATISTICAL IMAGE ANALYSIS OF SPOTTED ARRAYS
Authentication and Data Protection under Strong Adversarial Model

We are interested in addressing a series of existing and plausible threats to cybersecurity where the adversary possesses unconventional attack capabilities. Such unconventionality includes, in our exploration but not limited to, crowd-sourcing, physical/juridical coercion, substantial (but bounded) computational resources, malicious insiders, etc. Our studies show that unconventional adversaries can be counteracted with a special anchor of trust and/or a paradigm shift on a case-specific basis. Complementing cryptography, hardware security primitives are the last defense in the face of co-located (physical) and privileged (software) adversaries, hence serving as the special trust anchor. Examples of hardware primitives are architecture-shipped features (e.g., with CPU or chipsets), security chips or tokens, and certain features on peripheral/storage devices. We also propose changes of paradigm in conjunction with hardware primitives, such as containing attacks instead of counteracting, pretended compliance, and immunization instead of detection/prevention. In this thesis, we demonstrate how our philosophy is applied to cope with several exemplary scenarios of unconventional threats, and elaborate on the prototype systems we have implemented. Specifically, Gracewipe is designed for stealthy and verifiable secure deletion of on-disk user secrets under coercion; Hypnoguard protects in-RAM data when a computer is in sleep (ACPI S3) in case of various memory/guessing attacks; Uvauth mitigates large-scale human-assisted guessing attacks by receiving all login attempts in an indistinguishable manner, i.e., correct credentials in a legitimate session and incorrect ones in a plausible fake session; Inuksuk is proposed to protect user files against ransomware or other authorized tampering. It augments the hardware access control on self-encrypting drives with trusted execution to achieve data immunization. We have also extended the Gracewipe scenario to a network-based enterprise environment, aiming to address slightly different threats, e.g., malicious insiders. We believe the high-level methodology of these research topics can contribute to advancing the security research under strong adversarial assumptions, and the promotion of software-hardware orchestration in protecting execution integrity therein.
Document Oriented NoSQL Databases

For quite some time relational databases, such as MySQL, Oracle and Microsoft SQL Server, have been used to store data for most applications. While they are indeed ACID compliant (meaning interrupted database transactions won’t result in lost data or similar nasty surprises) and good at avoiding redundancy, they are difficult to scale horizontally (across multiple servers) and can be slow for certain tasks. With the Web growing rapidly, spawning enourmous, user-generated content websites such as Facebook and Twitter, fast databases that can handle huge amounts of data are a must. For this purpose new databases management systems collectively called NoSQL are being developed. This thesis explains NoSQL further and compares the write and retrieval speeds, as well as the space efficiency, of two database management systems from the document oriented branch of NoSQL called MongoDB and CouchDB, which both use the JavaScript Object Notation (JSON) to store their data within. The benchmarkings performed show that MongoDB is quite a lot faster than CouchDB, both when inserting and querying, when used with their respective Python libraries and dynamic queries. MongoDB also is more space efficient than CouchDB.
Incorporating Visual and Textual Cues in Dialogue Generation : An Application to Comic Strips

Conventional dialogue generation systems mostly leverage textual data and are incapable of detecting and acting on the visual cues while making conversation. Thus, they cannot be used to generate dialogue-oriented compositions such as scripts for television or comic strips which heavily use visual cues. In this work, we try to overcome this obstacle and propose a system which can make use of such cues to generate comic strips. First, we propose a baseline approach based on a conditional variational autoencoder which is only capable of predicting the last speech bubble of a strip. We then model the task as a visual story telling problem and adapt an encoderdecoder style model in order to generate entire comic strips. So as to test this story telling-based approach, we propose new metrics and also perform a qualitative human evaluation on the results. We notice that this model is able to detect the setting of a strip and the characters involved in most cases. It is also able to generate some coherent strips. We believe that the results are promising and that they warrant further research in this area.