Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of multimodal biometrie recognition using Iris and sclera

Biometrics ascribe to an automatic authentication of a person based on its individual features or characteristics. There is a huge variety of biometrics that can be used for identification and verification purpose. A biometric device is one that captures a physiological ‘image’ and uses that image to permit or deny access. Multimodal biometrics is used to refer a combination of two or more biometric modalities in an automatic authentication system. When used for identification and verification purposes biometrics systems provides better accuracy, security, improved reliability and convenience to the users than normal traditional based systems. This paper provides the survey of multimodal biometric evaluation using iris and sclera aspects of biometric identification on the basis of which evaluation of the biometric system is performed, the evolution of various parameters to enhance the recognition ability of a biometric method.
Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

This Report presents the results from EFSA project RC/EFSA/AMU/2016/01 related to the implementation of machine learning techniques for literature reviews and systematic reviews in EFSA. An overview of the different steps of a systematic review is provided, along with possible ways for automation. Although it was found that most steps could benefit from automation, it was also observed that some steps require more sophisticated methods than those encompassed within the machine learning framework. Availability of data and methodology allowed for the development of an automatic screening tool based on several machine learning techniques. The developed shiny R application can be used for the screening of abstracts and full texts. Properties of machine learning techniques are discussed in this Report together with their most important advantages and disadvantages. The latter discussion includes both general properties, as well as context-specific properties based on their performance in three case studies. Although creating a universal automatic data extraction tool was considered to be infeasible in this stage, this step of the systematic review was addressed to allow the reviewer to scan the uploaded pdf files for certain words or string of words. Based on observations from the performed case studies, recommendations were made regarding which methods are preferred in specific situations. More explicitly, a discussion is made about the performance of the classifiers with respect to the magnitude of the pool of papers to be screened as well as to the amount of imbalance, referring to the proportion of relevant and irrelevant papers. Finally, it was concluded that the results presented in this report provide proof that the developed shiny application could be efficiently used in combination with other software such as DistillerSR. © European Food Safety Authority, 2018
Editorial: Environmental flows in an uncertain future

Southern California Coastal Water Research Project, Costa Mesa, CA, United States, Water, Agriculture and Environment program, Jonathan Tonkin Population and Community Ecology, The University of Melbourne, Parkville, VIC, Australia, Riverfutures, Derbyshire, United Kingdom, Australian Rivers Institute, Griffith University, Nathan, QLD, Australia, University of Canterbury, Christchurch, New Zealand
The curiosity effect: Information seeking in the contemporary news environment

This research investigates how digital news headlines influence contemporary news information seeking. In two studies (a lab experiment and a field test), we examine how the presentation of news information—traditional, summary news headlines or clickbait, curiosity news headlines—influences the attitudinal and behavioral components of news seeking. Study 1 models the news-seeking process, finding that summary headlines heighten perceptions of headline information adequacy, which increase expectations that an article will provide clear information, which in turn increase anticipated audience engagement with news compared to some curiosity headlines. Study 2 determines that individuals’ selection behavior on nine local newspaper websites also favors summary headlines. The findings encourage researchers to employ information-seeking mechanisms in understanding news selection decisions.
Success and Satisfaction of Women in Financial Planning

There has been a lot of discussions, research, and initiatives about different aspects that relate to building the financial planning profession by attracting and retaining talented financial planners. An acknowledged problem and challenge for the profession is lack of ethnic and gender diversity. Surveys have shown that many people view financial planning as primarily a profession of white males. We know that the number of women financial planners has remained stagnant for the past decade and are curious as to reasons behind this lack of growth, especially since there is no evidence that women lack the skills or traits necessary to be a successful planner. Previous research has found evidence of sexism in the profession which includes significantly lower compensation for women, and different treatment of women by their fellow planners and employers. <br><br>We surveyed 224 experienced professional financial planners to analyze their feelings of satisfaction and success and how these feelings related to their gender, size of practice, education, personality traits, age, and years of experience. The women in our sample were equivalent or better than men with regards to education, experience, personality (the Big-Five Personality Traits), and CFP® certifications. However, we found that professional career satisfaction was surprisingly higher for women if they worked for a solo practice rather than for a large firm where both men and women felt more successful.
Performance indices of soft computing models to predict the heat load of buildings in terms of architectural indicators

This study estimates the heat load of buildings in Izmir/Turkey by three soft computing (SC) methods; Artificial Neural Networks (ANNs), Fuzzy Logic (FL) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) and compares their prediction indices. Obtaining knowledge about what the heat load of buildings would be in architectural design stage is necessary to forecast the building performance and take precautions against any possible failure. The best accuracy and prediction power of novel soft computing techniques would assist the practical way of this process. For this purpose, four inputs, namely, wall overall heat transfer coefficient, building area/ volume ratio, total external surface area and total window area/total external surface area ratio were employed in each model of this study. The predicted heat load is evaluated comparatively using simulation outputs. The ANN model estimated the heat load of the case apartments with a rate of 97.7% and the MAPE of 5.06%; while these ratios are 98.6% and 3.56% in Mamdani fuzzy inference systems (FL); 99.0% and 2.43% in ANFIS. When these values were compared, it was found that the ANFIS model has become the best learning technique among the others and can be applicable in building energy performance studies.
Corporate Governance on the Corporate Characteristics- Managerial Accounting link

The objective of this article attempts to investigate the intervening impact of corporate governance on the causal link between corporate characteristics and the application of managerial accounting, which has not been discussed and empirically investigated in the prior literature. A successful survey of 395 publicly listed firms in Vietnam offers experimental evidence that corporate characteristics indirectly contribute to the application of managerial accounting via corporate governance. This research further investigates whether the causal association from corporate governance to the application of managerial accounting is moderated by corporate characteristics, which has been ignored by prior research. The findings reveal that corporate characteristics impose a moderating influence on the corporate governance- managerial accounting causal linkage. The current research contributes to the management literature by shedding light on the intricate associations among corporate governance, corporate characteristics and the application of managerial accounting in that the mediation of corporate governance and the moderation of corporate characteristics are clarified. The findings are also expected useful to executives in helping for better decisions on the application of managerial accounting tools in business, appropriate to their corporate governance structure and corporate characteristics in order to receive the best possible corporate performance.
Passive localization of signal source based on wireless sensor network in the air

Passive localization of the wireless signal source attracts a considerable level of research interest for its wide applications in modern wireless communication systems. To accurately locate the signal source passively in the downtown area, sensors are carried on the unmanned aerial vehicles flying in the air, where the wireless sensor network can be established with an optimal geometry configuration conveniently. In this case, the influence of multipath fading can be avoided and the time difference of arrival measurement can be estimated precisely in Rician channel. By employing the operating center as a calibration source to refine the positions of the unmanned aerial vehicles, we present a simplified formulation of the time difference of arrival localization method according to the min-max criterion. To accurately estimate the position of the source, the nonlinear equations are relaxed using semidefinite programming to obtain an initial solution, which is utilized as the starting point of the iterative algorithm to refine the solution. In the simulation section, the validity and the robustness of the proposed methods are verified through the performance comparison with the Cramer–Rao lower bound.
Spaun 2.0: Extending the World’s Largest Functional Brain Model

Building large-scale brain models is one method used by theoretical neuroscientists to understand the way the human brain functions. Researchers typically use either a bottom-up approach, which focuses on the detailed modelling of various biological properties of the brain and places less importance on reproducing functional behaviour, or a top-down approach, which generally aim to reproduce the behaviour observed in real cognitive agents, but typically sacrifices adherence to constraints imposed by the neuro-biology. The focus of this thesis is Spaun, a large-scale brain model constructed using a combination of the bottom-up and top-down approaches to brain modelling. Spaun is currently the world’s largest functional brain model, capable of performing eight distinct cognitive tasks ranging from digit recognition to inductive reasoning. The thesis is organized to discuss three aspects of the Spaun model. First, it describes the original Spaun model, and explores how a top-down approach, known as the Semantic Pointer Architecture (SPA), has been combined with a bottom-up approach, known as the Neural Engineering Framework (NEF), to integrate six existing cognitive models into a unified cognitive model that is Spaun. Next, the thesis identifies some of the concerns with the original Spaun model, and show the modifications made to the network to remedy these issues. It also characterizes how the Spaun model was re-organized and re-implemented (to include the aforementioned modifications) as the Spaun 2.0 model. As part of the discussion of the Spaun 2.0 model, task performance results are presented that compare the original Spaun model and the re-implemented Spaun 2.0 model, demonstrating that the modifications to the Spaun 2.0 model have improved its accuracy on the working memory task, and the two induction tasks. Finally, three extensions to Spaun 2.0 are presented. These extensions take advantage of the re-organized Spaun model, giving Spaun 2.0 new capabilities – a motor system capable of adapting to unknown force fields applied to its arm; a visual system capable of processing 256×256 full-colour images; and the ability to follow general instructions. The Spaun model and architecture presented in this thesis demonstrate that by using the SPA and the NEF, it is not only possible to construct functional large-scale brain models, but to do so in a manner that supports complex extensions to the model. The final Spaun 2.0 model consists of approximately 6.6 million neurons, can perform 12 cognitive tasks, and has been demonstrated to reproduce behavioural and neurological data observed in natural cognitive agents.
Processing Polysomnographic Signals, using Independent Component Analysis

In this paper several applications of the Independent Component Analysis (ICA) algorithm, for the analysis of biomedical signal recordings have been investigated. One of these applications is the removal of EEG artifacts such as the EOG. It is shown that ICA may serve as a powerful tool, which could help the analysis of biomedical recordings, and give better insights about the underlying sources of some disorders. Another application of the proposed method is the detection of sleep disorders in patients suffering from sleep apnea. The ultimate goal of this approach is to develop an automatic noninvasive data acquisition system, for clinical applications.
Utility Divestitures in Germany: A Case Study of Corporate Financial Strategies and Energy Transition Risk

Germany is in the midst of a radical transformation of its power sector, which in 2016 led two of its main electric utilities, EON and RWE, to undertake dramatic restructurings. EON spun off its fossil fuel and trading segments, while RWE carved out its renewable energy, retail and grid business. The paper examines the drivers of these divestitures. Building on corporate finance literature, the paper uses a mix of comparative descriptive statistics, interviews and event studies to test four groups of hypotheses. The evidence rejects drivers related to operations and management, biased investment and investor preferences and instead points to financing-related drivers. Among the financing-related drivers, debt overhang and risk contamination seemed to have played the main role. Utilities restructured to save their healthy assets (renewables and grid infrastructure) from losses at their conventional power generation business (fossil fuel and nuclear plants). Already weakened from record losses in their fossil fuel powered generation fleet due to low electricity prices, after 2011 the nuclear exit emerged as an additional challenge to the utilities. Investors doubted the adequacy of utilities provisions for decommissioning nuclear power plants and storing toxic waste, and feared major cost increases for which the utilities would be unlimitedly liable. The paper uses existing research on divestitures in an empirical case that has implications for the evolution of European power markets. The results suggest that exiting conventional technologies as part of the transition to a more renewable energy mix might cause substantial costs. If these are not clarified and allocated ex ante, policy makers might find themselves forced to either burden tax payers or endanger utilities that are of systemic relevance to the energy sector.
Designing and performance of DSTATCOM with the help of ICCT to improve power quality with feeding of variety of loads (L-NL & Induction Motor loads)

This work presents a method capable of designing DSTATCOM to reduce harmonic distortion and correct the power factor to improve power quality. The use of alternating current circuits in electrical power system has been a common practice nearly since the very inception of the interconnected power network. The most familiar loads on such a system were the constant power, constant impedance and constant current loads or a linear combination of thereof. In these cases, the voltage and current wave shapes are nearly pure sinusoidal. But this is no longer the case with modern electric power system. Massive use of the nonlinear and time varying devices has led to distortion of voltage and current waveforms. As a consequence, recently the issue of power quality has become important. Both electric utility and end users of electric power are becoming increasingly concerned about the quality of electric power. The term “power quality” has been used to describe the variation of the voltage, current and frequency on the power system beyond a limit. The performance of the system is simulated for Linear and Motor load.
Transaction Costs, Portfolio Characteristics, and Mutual Fund Performance

We study the interdependencies between transaction costs, portfolio characteristics, and mutual fund performance. Using a novel data set of actual mutual fund trades, we find that, controlling for investment style, larger funds realize lower percentage transaction costs than smaller funds. Larger mutual funds trade less frequently and hold bigger stocks to actively avoid incurring higher trading costs. Gross returns of larger funds are lower than those of smaller funds due, in part, to the characteristics of their holdings, which suggests that decreasing returns to scale could arise due to limited investment opportunities because of trading cost constraints. Taken together, our results highlight the trade-offs faced by mutual funds between trading costs, portfolio characteristics, and fund performance as they grow in size. This paper was accepted by Karl Diether, finance.
Digital signal processing and digital-to-analog converters for wide-band transmitters

In this thesis, the implementation methods of digital signal processing and digitalto-analog converters for wide-band transmitters are researched. With digital signal processing, the problems of analog signal processing, such as sensitivity to interference and nonidealities of the semiconductor processes, can be avoided. Also, the programmability can be implemented digitally more easily than by means of analog signal processing. During the past few years, wireless communications has evolved from analog to digital, and signal bandwidths have increased, enabling faster and faster data transmission. The evolution of semiconductor processes, decreasing linewidth and supply voltages, has decreased the size of the electronics and power dissipation, enabling the integration of larger and larger systems on single silicon chips. There is little overall benefit in decreasing linewidths to meet the needs of analog design, since it makes the design process more difficult as the device sizes cannot be scaled according to minimum linewidth and because of the decreasing supply voltage. On the other hand, the challenges of digital signal processing are related to the efficient realization of signal processing algorithms in such a way that the required area and power dissipation does not increase extensively. In this book, the problems related to digital filters, upconversion algorithms and digital-to-analog converters used in digital transmitters are researched. Research results are applied to the implementation of a transmitter for a third-generation WCDMA base-station. In addition, the theory of factors affecting the linearity and performance of digitalto analog converters is researched, and a digital calibration algorithm for enhancement of the static linearity has been presented. The algorithm has been implemented together with a 16-bit converter; its functionality has been demonstrated with measurements.
Is takaful industry innovative? Examining the perception of Takaful operators in Malaysia

Takaful is a social and ethical insurance based on the principle of Ta’awun (cooperation) and Tabarru (donation) where the risk is shared collectively by members of a policy. The concept of Takaful is providing financial assistance to the participants on a basis for mutual assistance, brotherhood and solidarity in cases where the participants face some misfortune. Takaful is an alternative to the conventional insurance, and its primary objective is to protect the participants based on the ethical and moral foundations. Due to its unique innovative and not facing any major issues at the moment. They also suggested various possible solutions to the current situation which indirectly shows that there are many aspects that can be improved in the Takaful industry to achieve innovation that meets consumer requirements and satisfaction. characteristics, Takaful is one of the fastest growing areas of International finance. An industry that started with less than 10 players in the early eighties has become a multi-billion-dollar global industry that currently spans across South-East Asia, Middle East, Africa, Central Asia and Europe with more than 200 players. The purpose of this research is to understand how Takaful operators view Takaful products that are being created with innovative methods and techniques in Malaysia. As compare to most of the prior research that are centred on the demand side (i.e. consumers), this research however focuses on the supply side of Takaful industry (i.e. the Takaful operators). The findings of this research are significant as it pushes the industry to be innovative in their products and services. Not only innovative products are good to the customer, but also enhance the Malaysian Takaful products to become global models. To achieve this objective, in-depth and semi-structured interviews were conducted with the relevant fourteen officers among the Takaful Operators (TOs) to obtain their views regarding the objective. From the findings, it can be seen that there is a negative trend of innovation in the Takaful industry as the respondents highlighted a lot of issues and challenges that hamper innovation in the whole industry. Only a few respondents thought that the industry is already
Sentiments Analysis of Reviews Based on ARCNN Model

The sentiments analysis of product reviews is designed to help customers understand the status of the product. The traditional method of sentiments analysis relies on the input of a fixed feature vector which is performance bottleneck of the basic codec architecture. In this paper, we propose an attention mechanism with BRNN-CNN model, referring to as ARCNN model. In order to have a good analysis of the semantic relations between words and solves the problem of dimension disaster, we use the GloVe algorithm to train the vector representations for words. Then, ARCNN model is proposed to deal with the problem of deep features training. Specifically, BRNN model is proposed to investigate non-fixed-length vectors and keep time series information perfectly and CNN can study more connection of deep semantic links. Moreover, the attention mechanism can automatically learn from the data and optimize the allocation of weights. Finally, a softmax classifier is designed to complete the sentiment classification of reviews. Experiments show that the proposed method can improve the accuracy of sentiment classification compared with benchmark methods.
Performance Evaluation of Low Cost LoRa Modules in IoT Applications

LoRa is a low power long range wireless communication platform that is designed as an efficient communication platform for small, low powered devices. This makes it very suitable for battery powered devices and IoT implementation. This paper evaluates some low cost LoRa modules available on the market and their suitability, energy efficiency and performance during operation. Two low cost LoRa transceiver from Semtech Industries, the SX1272 and SX1278 were tested for their power consumption and maximum transmission range. This study have evaluated the two LoRa solutions and found that the SX1278 have a better transmission range and uses lower energy compared to the SX1272 thus making it more suitable for embedded implementation as a data gateway.