Miracle-dz/UniFAR-FTUs huggingface dataset:
An ontology-driven evidence theory method for activity recognition

Activity recognition is a vital need in the field of ambient intelligence. It is essential for many internet of things applications including energy management, healthcare systems and home automation. But, even with the many cheap mobile sensors envisioned by the internet of things, activity recognition remains a hard problem. This is due to uncertainty in sensor readings and the complexity of activities themselves. Evidence theory models provide activity recognition even in the presence of uncertain sensor readings, but cannot yet model complex activities or dynamic changes in sensor and environment configurations. This work proposes combining knowledge-based approaches with evidence theory, improving the construction of evidence theory models for activity recognition by bringing reusability, flexibility and rich semantics.
Novel applications and methods for the computer-aided understanding and design of enzyme activity

Metabolic engineering efforts require enzymes that are both highly active and specific towards the synthesis of a desired output product in order to be commercially feasible. The 3hydroxyacid (3HA) or coenzyme-A dependent chain elongation pathway can allow for the synthesis of dozens of useful compounds of various chain lengths and functionalities, but suffers from byproduct formation, which lowers yields of the desired longer chain products, as well as increases downstream separation costs. The thiolase enzyme catalyzes the first reaction in this pathway, and its substrate specificity at each of its two catalytic steps sets the chain length and composition of the chemical scaffold upon which the other downstream enzymes act. However, there have been few attempts reported in the literature to rationally engineer thiolase substrate specificity. In this work, we present a model-guided, rational design study of ordered substrate binding applied to two biosynthetic thiolases, with the goal of increasing the ratio of C6/C4 products formed by the 3HA pathway, 3-hydroxy-hexanoic acid (3HH) and 3-hydroxybutyric acid (3HB). We identify thiolase mutants that result in nearly ten-fold increases in C6/C4 selectivity. Our findings can extend to other pathways that employ the thiolase for chain elongation, as well as expand our knowledge of sequence-structure-function relationship for this important class of enzymes.
Accurate Vehicle Localization and Traveling Lane Classification by Aligning Laser Scanner Data Sequences

We propose a method for accurate vehicle localization. The proposed method detects a vehicle’s location and traveling lane by matching between a pre-constructed Range Data Map and laser scanner data sequences measured while the vehicle runs. The Range Data Map consists of an absolute position on the road and range data at the position. We use Dynamic Time Warping (DTW) to align multiple range data sequences. Experiments using data sequences collected while a vehicle ran on the same route with multiple traffic lanes were conducted. The results demonstrated the effectiveness of vehicle localization and traveling lane classification by the proposed method.
The Geography of Lender Behavior in Peer‐To‐Peer Loan Markets

Theoretical and empirical research on the traditional credit market finds strong evidence that investors and lenders are sensitive to their distance from the borrower, especially early on in a venture. This is due to the cost of information gathering and monitoring. However, new online platforms could overcome the geographical constraints on investing. Recent empirical work, across many types of crowdfunding, has found mixed results. In this paper, using transaction data from a peer-to-peer lending site, I find that local lenders tend to bid earlier, both chronologically and relatively to other bidders in the auction, and bid larger amounts than nonlocal lenders. Additionally, local lenders are more informed in the sense that they are better able to evaluate the underlying risk of borrowers. This is demonstrated by the fact that they bid significantly higher interest rates on loans that ex-post default and lower rates on loans that ex-post pay back in full. Lastly, I develop a simple model of social learning with heterogeneous agents that provides testable predictions. My results are consistent with this model; a listing with more early local bidding activity will attract more lenders, leading to a higher probability of funding and a lower final interest rate, if funded. This work suggests that the behavioral differences between local and nonlocal lenders are driven mostly by informational frictions and not merely preferences. Local lenders are better informed because they have easier and cheaper access to information, and this asymmetry contributes to explaining why geographic-based frictions are still present and relevant in online lending markets.
Impact of Institutional Factors on Foreign Direct Investment Inflows: Cross-country Analysis

This research paper is devoted to analysis of various institutional factors as determinants of foreign direct investment (further – FDI) inflows to different countries. The objective of the research is to estimate the effect of institutions on FDI inflows. The analysis is provided on a database of cross-country FDI inflows on 72 countries FDI-importers and 112 countries FDI-exporters in the period from 2001 to 2016. It is supposed in the paper that the impact of institutional factors might be different for the groups of developed and developing countries; since developed economies have higher institutional indicators, they tend to attract larger amounts of foreign direct investment compared to developing economies, where institutional development is at the lower level. The estimation is based on the gravity approach, which considers the positive effects of countries’ GDP and the negative effect of the distance between them. The main method used for the econometric estimation is the Pseudo Poisson Maximum Likelihood (PPML) regression, which is considered to be one of the adequate methods for estimating such data. During the research the problems of zero-observations and correlation between institutional indicators are solved. The results have shown that higher quality of institutions tends to attract more foreign direct investment to a country. Thus, institutions in developed countries have positive and significant impact on FDI attraction. At the same time, the analysis of developing countries has shown that some institutions have less significant influence on the FDI inflows. Based on the results of the research, possible recommendations for government policy on institutional improvement can be suggested. JEL Classification Numbers: F21, DOI: 10.12955/cbup.v7. 1348
PERAMALAN HARGA SAHAM MENGGUNAKAN JARINGAN SYARAF TIRUAN SECARA SUPERVISED LEARNING DENGAN ALGORITMA BACKPROPAGATION

Stock price prediction is useful for investors to see how the prospects of a company's stock investment in the future. Stock price prediction can be used to anticipate the deviation of stock prices. It can also helps investors in decision making. Artificial Neural Networks do not require mathematical models but data from problems to be solved. Information is conveyed through the data, and the Artificial Neural Network filters the information through training. Therefore, Artificial Neural Network is appropriate to solve the problem of stock price prediction. Learning method that will be used to predict stock price is Supervised Learning with Backpropagation algorithm. With this algorithm, networks can be trained using stock price data from the previous time, classify it and adjust network link weight as new input and forecast future stock prices. By using ANN, time series prediction is more accurate. After analyzing the problem of stock price movement system, the writer can know the pattern of what variables will be taken for further insert into the stock price forecasting system. This application can be used for stock price forecasting technique, so it will be useful for beginner investor as well as advanced investor as reference to invest in capital market. Implementing supervised learning backpropagation method will get accurate forecasting results more than 98%. Keyword - artificial neural network, stock, backpropagation.
Exploring the role of decision-making factors in international student marketing engagement

ABSTRACT This study applies the pre-purchase decision-making model for services to higher education context in order to understand international students' post-acquisition marketing engagement. The research categorizes three types of decision-making factors (i.e. internal, external, and risk) and two types of customer marketing engagement (CME) (i.e. in-role and extra-role). Data from 174 U.S. schools with international student engagement shows that student's internal factor has significantly relationship with all five dimensions of CME (i.e. interactive, affective, calculative, persuasive, and benevolent). The difficulty of the program has least influences, but all three types of decision making factors affect persuasive engagement. Student satisfaction moderates the relationship between in-role and extra-role CME. Theoretical, empirical, and managerial contribution are discussed subsequently.
Decision Analytics and Decentralized Ledger Technologies for Determination and Preservation of Spare Part Value in Aircraft Maintenance

Aircraft spare parts are used to quickly replace defective parts and ideally avoid expensive aircraft-on-ground situations. Understanding the Fair Market Value of surplus parts is of eminent importance for the competitive advantage of a company. Decisions such as purchase, sale, storage or scrapping are made on the basis of the determined value. Domain experts state that the value of a part depends significantly on its specific characteristics, condition and workshop event history. If the documentation of this history is incomplete, this can lead to a complete loss of value of the part, since, for example, safety-relevant parts may no longer be used without complete documentation. For companies that want to be able to survive in the highly competitive Maintenance Repair and Overhaul market, the use of digital technologies for data-based decision making has become unavoidable. The drowning of data while at the same time thirsting for information affects all market participants who manage their spare parts using digital technologies. The competitive advantage over others is now to use this data efficiently and make decisions based on data rather than experience and instinct. On the other hand, processes still exist in this industry that require documentation in paper form. One such process is the documentation of workshop events for safety-relevant spare parts by means of certificates. Low mutual trust and the heterogeneity of regional requirements in a global market prohibit the establishment of a central instance for data management. The determination of a Fair Market Value was carried out manually for a long time, with great personnel effort and low reliability. The design of an Automated Spare Part Valuation concept provides a basis for data owners to use the amount of data reliably. Similar implementations in industry and with integrated automated evaluation prove the usability. The problem of incomplete certificates of workshop events is addressed and solved by the conception, implementation and evaluation of a Blockchain-based Certification System. The characteristics of a blockchain, in particular its decentralization and persistence, meet the requirements that could not previously be met in an environment with a lack of trust and due to the danger of a single point of failure.
Efficient, Practical Dynamic Program Analyses for Concurrency Correctness

Shared-memory parallel programs are notoriously difficult to be both scalable and correct. One of the most problematic concurrency bugs is data race. Data races are difficult to avoid, find, fix, reproduce, and eliminate. A fundamental problem is that language and hardware memory models provide few or no guarantees for executions containing data races. Researchers have developed various program analyses and runtime tools for concurrency correctness properties. Examples includes data race detectors, multithreaded record & replay, transactional memory, and enforcement of stronger memory models. However, in the presence of data races, many of these tools suffer from limitations that impede their widespread use. The first challenge in handling racy executions is the high overhead for tracking (i.e., detect or control) cross-thread dependences, which is a necessary requirement to ensure the soundness of many analyses. The second limitation is that existing work has not covered the full range of possible behaviors for racy executions in weak memory models. This thesis explores several efficient and practical dynamic program analyses that aim to overcome these two key limitations, advancing the state of the art for detecting, enforcing, and exposing issues related to concurrency correctness. We present hybrid tracking and RegPlay to address the first challenge. Hybrid tracking is a generalized framework for tracking dependences, which hybridizes pessimistic and optimistic tracking in order to get the best of both world. We build hybrid-tracking-based
On Kernel-base Multi-Task Learning

Multi-Task Learning (MTL) has been an active research area in machine learning for two decades. By training multiple relevant tasks simultaneously with information shared across tasks, it is possible to improve the generalization performance of each task, compared to training each individual task independently. During the past decade, most MTL research has been based on the Regularization-Loss framework due to its flexibility in specifying various types of information sharing strategies, the opportunity it offers to yield a kernel-based methods and its capability in promoting sparse feature representations. However, certain limitations exist in both theoretical and practical aspects of Regularization-Lossbased MTL. Theoretically, previous research on generalization bounds in connection to MTL Hypothesis Space (HS)s, where data of all tasks are pre-processed by a (partially) common operator, has been limited in two aspects: First, all previous works assumed linearity of the operator, therefore completely excluding kernel-based MTL HSs, for which the operator is potentially nonlinear. Secondly, all previous works, rather unnecessarily, assumed that all the task weights to be constrained within norm-balls, whose radii are equal. The requirement of equal radii leads to significant inflexibility of the relevant HSs, which may cause the generalization performance of the corresponding MTL models to deteriorate. Practically, various algorithms have been developed for kernel-based MTL models, due to different characteristics of the formulations. Most of these algorithms are a burden to develop and end up being quite sophisticated, so that practitioners may face a hard task in interpreting and implementing them, especially when multiple models are involved. This is even more so, when Multi-Task Multiple Kernel Learning (MT-MKL) models are considered. This research largely resolves the above limitations. Theoretically, a pair of new kernel-based iii HSs are proposed: one for single-kernel MTL, and another one for MT-MKL. Unlike previous works, we allow each task weight to be constrained within a norm-ball, whose radius is learned during training. By deriving and analyzing the generalization bounds of these two HSs, we show that, indeed, such a flexibility leads to much tighter generalization bounds, which often results to significantly better generalization performance. Based on this observation, a pair of new models is developed, one for each case: single-kernel MTL, and another one for MT-MKL. From a practical perspective, we propose a general MT-MKL framework that covers most of the prominent MT-MKL approaches, including our new MT-MKL formulation. Then, a general purpose algorithm is developed to solve the framework, which can also be employed for training all other models subsumed by this framework. A series of experiments is conducted to assess the merits of the proposed mode when trained by the new algorithm. Certain properties of our HSs and formulations are demonstrated, and the advantage of our model in terms of classification accuracy is shown via these experiments.
Drivers to energy efficiency development in lighting and air-conditioning systems in manufacturing industries in Ghana for 2018

The increase in electricity demand, coupled with drastic deficit in energy generation and depleting conventional energy resources continues to create complex challenges for the energy market in Ghana. But Energy Efficiency (EE) in lighting and air-conditioning has been considered as a green area for reducing energy consumption. The manufacturing sector has been considered as a key area for the implementation of energy efficiency practices. This paper presents a survey to assess the drivers to energy efficiency in lighting and air-conditioning systems from the perspective of two manufacturing industries. Questionnaires were distributed to 260 employees in two manufacturing industries in Ghana. Key informant interviews were carried with four respondents. The impact of employee gender, department of work and job position in mediating the drivers of EE was also assessed using correlation analysis. The results showed that the staff of manufacturing industries sees the availability of information on energy efficiency measures, staff awareness and knowledge and the availability of funds as leading driversto energy efficiency development. Also providing incentives in the form of awards to employees for energy efficiency participation was shown to be effective in the implementation of EE measures.The inferential statistics showed that employees’ gender, department and job position predict the barriers to energy efficiency in the manufacturing industries. However, few of the drivers of energy efficiency are not dependent on gender, department of work and job position. Key words: Energy efficiency, energy consumption, drivers.
Dynamic suppression of sensory detail saves energy

—High functioning autistic people can exhibit excep- tional skills with numbers, eidetic imagery and recall of concrete detail, as brought to popular attention in the ﬁlm Rain Man. However, it now transpires that these skills are to some extent latent within all of us. We do not have access under normal circumstances to this concrete detail, because it is dynamically inhibited by higher level concepts. Brain stimulation using Trans Cranial Magnetic Stimulation or Direct Current Stimulation, which blocks this inhibition, releases savant-like skills in non- savants. This paper proposes that one of the reasons for this lies in the brain’s need to conserve energy. Computer simulations using a spiking neural network support this hypothesis. A spiking neural network was set up with a number of feature detectors feeding an output unit, which in turn generates inhibition of the input neurons. This reduces the spike activity of the input, and thus overall energy usage. We introduce a theoretical analysis for the gains, which might be made. Thus, we demonstrate that energy conservation is a possible cause of inhibition of sensory detail by high level concepts.
Building Language and Literacy Foundations for L2 Learners in West Africa with Oral Vocabulary Instruction and Shared Reading of Predictable and Non-Predictable Stories in Picture Books

ii Acknowledgments . iii List of Tables . vii List of Illustrations . viii List of Abbreviations . ix Chapter 1: INTRODUCTION.1 Purpose of the Study.1 Review of the Problem.1 Discussion of the Hypotheses.6 Theoretical Significance.7 Practical Importance.7 Description of Research Context.9 Design, Variables, and Materials.11 Limitations.13 Definition of Terms . 14 Chapter 2: REVIEW OF RELATED LITERATURE.16 Problem Explored.16 Theoretical Considerations.17 Cognitive Psychology and Learning Theory.17
Experimental and Numerical Investigation for R/C Portal Frames Tested Under Uniform Load

This paper presents an experimental and numerical investigations for three reinforced self-compacting concrete portal frames tested under uniform load. Control frame was tested without any types of strengthening, while second frame was strengthened with CFRP-sheet with dimensions (75 * 1000) mm at the bottom of beam and with dimensions (75 *700) mm at both joints, (350 mm) extending to the upper surface of the beam and (350 mm) on the outer face of the column. The last frame was cast in two pours tested without strengthening. The experimental tests showed that strengthened frame had stiffness more than control frame and its ultimate load was more than control frame by (19.45 %), while frame with two pours has an ultimate load less than a control frame with (11.26 %), however stiffness seem approximately to the control frame. Deflection for strengthened frame was less than control frame by (4.76%), on the other hand frame with two pours deflection was more than control frame by (4.55 %). A numerical finite element program ultimate load was more than experimental by about (4.28 %).
A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit

This article develops an integrated methodology to investigate dominant trajectories of neighborhood change that are often confronted in urban studies. Currently, researchers are using k-means cluster analysis to establish diverse neighborhood typologies and principal component analysis (PCA) to identify socioeconomic interactions explaining the neighborhood typologies. Little attention has been given to longitudinal trajectories and dynamics of neighborhood evolution over a long period. Our new model adapts a newly developed dynamic sequential analysis (the weighted minimum edit distance algorithm) in big data analytics to sort and identify dominant trajectories of neighborhood change. Our model also innovatively synthesizes three statistical procedures—k-means, PCA, and analysis of variance—to derive the weight matrix, which naturally integrates the core characteristics of urban neighborhood changes into the sequential reordering. Using the census data in Metro Detroit over five census years (1970, 1980, 1990, 2000, and 2010), this model was tested to identify a unique city's demographic and socioeconomic transition pattern in the past forty years. This model successfully provided a thorough analysis of the neighborhood typologies and exhibited a much-enhanced performance in identifying long-term trajectories of urban evolution.