Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Audit Fees Adjust Quickly?—Evidence from India

Abstract The article empirically analyses the partial adjustment in audit fees for top 500 National Stock Exchange-listed firms in India during 2004–2015. The findings indicate that firm characteristics have low explanatory power in predicting year-on-year changes in audit fees in accordance with sticky price literature in economics. We also find evidence of stickiness in audit prices with respect to changes in firm size which is in line with the signalling and credence good theories. The downward stickiness in audit fee may indicate overcharging in the market. However, audit fee stickiness ceases to exist for the set of group firms, highlighting the importance of ownership structure in affecting firm outcomes in India. Recognizing stickiness is important for managers, as it has implications for the audit fee negotiation process.
SECURING THE SDN NORTHBOUND INTERFACE WITH THE AID OF ANOMALY DETECTION

Software defined networking is an active research topic. Most research focuses on functionality, and security of the SDN infrastructure has only recently gained attention. However, research is mainly focused on the southbound interface. The SDN northbound interface, required for SDN applications to communicate with the controller has received few security attention. This research identifies the most important security features needed for a northbound interface to be secure. We tested several popular open-source SDN controllers for their support of these features. It shows that the overall status of these controllers is poor with regards to the security of their northbound interfaces. While some popular controllers support most important security features, they are almost all disabled by default, requiring some additional configuration. Other controllers offer few or no security features. An important feature missing for all controllers is authorization, the ability to restrict to which parts of the northbound interface an application has access. Also important is the case of the hacked application, which, by using the northbound interface, can disrupt the network without being detected or stopped. We propose a solution for detecting this, by using statistical anomaly detection. We have demonstrated some advantages of this solution, by using a prototype implementation. However, this solution requires more testing and validation to be fully usable.
The Cambridge Atlas of Astronomy

Jean Audouze and Guy Israel (eds) 1985 Cambridge: Cambridge University Press 432 pp price £40 ISBN 0 521 26369 7 This is an attractive book crammed full of current astronomical and astrophysical information. It is very highly illustrated with diagrams and monochrome and full colour photographs, presented in an attractive large format.
Partisan Conflict and Income Distribution in the United States: A Nonparametric Causality-in-Quantiles Approach

This study examines the predictive power of a partisan conflict index on income inequality. Our study adds to the existing literature by using the newly introduced nonparametric causality-in-quantile testing approach to examine how political polarization in the Unites States affects several measures of income inequality and distribution overtime. The study uses annual time-series data from 1917-2013. We find evidence of a causal relationship running from partisan conflict to income inequality, except at the upper end of the quantiles. The study suggests that a reduction in partisan conflict will lead to a more equal income distribution.
A Mathematical Programming Approach for Imputation of Unknown Journal Ratings in a Combined Journal Quality List

The quality of faculty scholarship and productivity is one of the primary measures for faculty evaluation in most academic institutions. Due to the diversity and interdisciplinary nature of modern academic research ﬁelds, it is increasingly important to use journal quality lists, with journal ratings, that oﬀer credible measures of the worth of faculty scholarship. Despite the existence of such metrics, journal lists, by their very nature, exclude some well-recognized journals. Consequently, academic institutions expend inordinate resources to assess the quality of unrated journals appropriately and equitably across disciplines. The current research proposes mathematical programming models as a path to determining unknown ratings of multiple journal quality lists, using only their known rating information. The objective of the models is to minimize the total number of instances where two journals are rated in opposite order by two diﬀerent journal quality lists. Computational results based on journal quality list data in https://harzing.com/ indicate that the proposed methods outperform existing imputation algorithms with most realistic test datasets in terms of accuracy, root mean square error, and mean absolute deviation.
A real-time face recognition for class participation enrollment system over WebRTC

In the classroom, students can get the most benefit for themselves when attend and participate in the classroom. Roll-call is a classical method that mostly uses for the class participation enrollment. The time that used for this method is depended on the number of students; the more number of students, the more time to spend. This work presents the method that improves the class participation enrollment process Thus, we developed the face detection and face recognition system by applying the WebRTC. Since it is a platform independent, we could capture the participant faces from anywhere without an installation. In addition, the three standard face detection and recognition algorithms were applied in two main processes properly. The result showed that system can improve the class participation enrollment accuracy to be more precise and persuaded the student to attend the class as well. Moreover, the system can install to the classroom easily because it is developed in form of the web application and needs an only web camera for the additional device.
A Cointegration Analysis of Agricultural, Energy and Bio-Fuel Spot and Futures Prices

This paper features an analysis of the cointegration relationships among agricultural commodity, ethanol and Cushing crude oil spot and futures prices. The use of grains for the creation of bio-fuels has sparked fears that these demands are inflating food prices. We analyse approximately 10 years of daily spot and futures prices for corn, wheat, sugar ethanol and oil prices from Datastream for the period 19 July 2006 to 2 July 2015. The analysis, featuring Engle-Granger pairwise cointegration and Markov-switching VECM and Impulse Response Analysis, confirms that these markets have significant linkages which vary according to whether they are in low or high volatility regimes.
Matching-Based Assignement Strategies for Improving Data Locality of Map Tasks in MapReduce

MapReduce is a well-know framework for distributing data-processing computations onto parallel clusters. In MapReduce, a large computation is broken into small tasks that run in parallel on multiple machines, and scales easily to very large clusters of inexpensive commodity computers. Before the Map phase, the original dataset is split into data chunks that are replicated (a constant number of times, usually 3) and distributed randomly onto computing nodes. During the Map phase, local tasks (i.e., tasks whose data chunks are stored locally) are assigned in priority when processors request tasks. In this paper, we provide the first complete theoretical analysis of data locality in the Map phase of MapReduce, and more generally, for bag-of-tasks applications that behave like MapReduce. We prove that if tasks are homogeneous (in terms of processing time), as soon as the replication factor is larger than 2, FindAssignment, a matching based algorithm, achieves a quasi-perfect makespan (i.e., optimal up to an additive constant of one step) using a sophisticated matching algorithm. Above result is proved with high probability when the number of tasks becomes arbitrarily large, and we therefore complement theoretical results with simulations that corroborate them even for small number of tasks. We also show that the matching-based approach leads to an improvement of data locality during the Map phase and therefore decreases the amount of communications needed to achieve perfect makespan, compared to the classical MapReduce greedy approach.
Bionic design of the quadrupedal robot and motion simulation

Mechanical structure of the quadrupedal robot was designed to complete pitch motion by mimicking the mammal's body. Joint motion tracks of the quadrupedal robot were designed and motion relation of knee and hip joints was formulated according to the leg-motion-pattern of mammal. Rhythmic motion controller of the quadrupedal robot adopted biological model of central pattern generator (CPG), whose outputs were used to be control signals of joint angles for hips. Flipping and translating the motion curves of hip joints in the same leg obtained control signals of knee joints. Proved by numerical and animation simulation, all the designs for the quadrupedal robot are effective. The quadrupedal robot can walk smoothly.
Handbook of Functional MRI Data Analysis: Statistical modeling: Single subject analysis

The goal of an fMRI data analysis is to analyze each voxel's time series to see whether the BOLD signal changes in response to some manipulation. For example, if a stimulus was repeatedly presented to a subject in a blocked fashion, following the trend shown in the red line in the top panel of Figure 5.1, we would search for voxel time series that match this pattern, such as the BOLD signal shown in blue. The tool used to fit and detect this variation is the general linear model (GLM), where the BOLD time series plays the role of dependent variable, and the independent variables in the model reflect the expected BOLD stimulus timecourses. Observe, though, that square wave predictor in red doesn't follow the BOLD data very well, due to sluggish response of the physiology. This leads to one major focus of this chapter: Using our understanding of the BOLD response to create GLM predictors that will model the BOLD signal as accurately as possible. The other focus is modeling and accounting for BOLD noise and other souces of variation in fMRI time series. Throughout this chapter the models being discussed will refer to modeling the BOLD signal in a single voxel in the brain. Such a voxel-by-voxel approach is known as a mass univariate data analysis, in contrast to a multivariate approach (see Chapters 8 and 9 for uses of multivariate models).
Stress Testing the Equity Home Bias: A Turnover Analysis of Eurozone Markets

Abstract Shifts in equity turnover happen on and around holidays because rationally bounded investors become distracted. Their pattern reveals a persistent equity home bias even in the Eurozone, a stress test case for the survival of this bias given the high level of economic and financial development and integration in this area. The bias is greater for small caps because investors are reluctant to hold this class of foreign asset. Our study corroborates calendar anomalies in trading volumes, but refutes the hypothesis regarding turnover sensitivity to stock returns common in the empirical and theoretical literature based on investor heterogeneity and short sale constraints. Our results reveal vanishing cost asymmetries in taking long rather than short positions.
Digital image searching: Strategies and preferences of graduate students-implications for the DASe Collection

This paper examines the image retrieval habits and preferences of The University of Texas at Austin graduate students as well as users’ satisfactions with the search functions and organization of the Digital Archive Services Collection (DASe). The study sought to explore potential causes of the low usage rate of the DASe, an image database created for the faculty and students at the University of Texas. Collected data demonstrates aspects of the DASe database that could be improved and modified to align with expectations and habits of graduate students in order to increase the level of use.
Design of a compact low-power human-computer interaction equipment for hand motion

Human-Computer Interaction (HCI) raises demand of convenience, endurance, responsiveness and naturalness. This paper describes a design of a compact wearable low-power HCI equipment applied to gesture recognition. System combines multi-mode sense signals: the vision sense signal and the motion sense signal, and the equipment is equipped with the depth camera and the motion sensor. The dimension (40 mm × 30 mm) and structure is compact and portable after tight integration. System is built on a module layered framework, which contributes to real-time collection (60 fps), process and transmission via synchronous confusion with asynchronous concurrent collection and wireless Blue 4.0 transmission. To minimize equipment’s energy consumption, system makes use of low-power components, managing peripheral state dynamically, switching into idle mode intelligently, pulse-width modulation (PWM) of the NIR LEDs of the depth camera and algorithm optimization by the motion sensor. To test this equipment’s function and performance, a gesture recognition algorithm is applied to system. As the result presents, general energy consumption could be as low as 0.5 W.
High Frequency Volatility Co-Movements in Cryptocurrency Markets

Through the application of Diagonal BEKK and Asymmetric Diagonal BEKK methodologies to intra-day data for eight cryptocurrencies, this paper investigates not only conditional volatility dynamics of major cryptocurrencies, but also their volatility co-movements. We first provide evidence that all conditional variances are significantly affected by both previous squared errors and past conditional volatility. It is also shown that both methodologies indicate that cryptocurrency investors pay the most attention to news relating to Neo and the least attention to news relating to Dash, while shocks in OmiseGo persist the least and shocks in Bitcoin persist the most, although all of the considered cryptocurrencies possess high levels of persistence of volatility over time. We also demonstrate that the conditional covariances are significantly affected by both cross-products of past error terms and past conditional covariances, suggesting strong interdependencies between cryptocurrencies. It is also demonstrated that the Asymmetric Diagonal BEKK model is a superior choice of methodology, with our results suggesting significant asymmetric effects of positive and negative shocks in the conditional volatility of the price returns of all of our investigated cryptocurrencies, while the conditional covariances capture asymmetric effects of good and bad news accordingly. Finally, it is shown that time-varying conditional correlations exist, with our selected cryptocurrencies being strongly positively correlated, further highlighting interdependencies within cryptocurrency markets.
CONTEMPORARY DATA ANALYSIS TECHNIQUES FOR ONLINE REPUTATION MANAGEMENT IN HOSPITALITY AND TOURISM

Knowing what attracts or deters tourists to/from a tourist visit and what products to offer them and to pay special attention to is crucial for good economic results. Such knowledge can be obtained by analysis of online comments and reviews that tourists leave on travel websites (such as Booking, TripAdvisor, Trivago, etc.). This paper describes the value which information about opinions and emotions hidden in online reviews has for managers who receive it, especially the knowledge of (dis)satisfaction of users with certain aspects of the tourist offer. Uncovered knowledge from online reviews provides a chance to take advantage of the strong points, and correct the shortcomings through timely corrective measures and actions. Contemporary approaches and methods of analyzing online reviews and the opportunities for development they provide in the tourism industry are described through a case study conducted over a subset of 20491 hotel reviews from TripAdvisor. We have conducted sentiment analysis of reviews with the goal of building an automated model which will successfully distinguish positive from negative reviews. Logistic Regression classifier has the best performance, in 90% of reviews it has correctly classified positive reviews and in 83% negative. We have illustrated how association rules can help management to uncover relationships between concepts under discussion in negative and positive reviews.
Bodybuilders as Vigilantes and Vigilantes as Perpetrators of Election Violence: True?

In Sub-Saharan Africa, bodybuilders have been demonized as Vigilantes who are not violence averse, and who cause election violence. The situation is more complex and does not provide a straight forward cause and effect approach. This is an investigation into the relationship between bodybuilding and political party vigilantism and the role these actors play in election violence. The author used mixed methodology of analytical literature review and ethnographical investigative tool. Observer-Participant studied 16 bodybuilders in an Accra City Gym to determine if bodybuilding predisposes one to Political Party Vigilantism and consequently to election violence. Prompted by the generally known fact that some recreational and professional athletes’ use Anabolic-Androgenic Steroids, AAS for performance enhancement and image improvement, the association between AAS and increased aggression and violence was reviewed (Hoskin & Ellis, 2015). The investigation appears to suggest that the national media may be justified for painting some bodybuilders as testosterone driven, thrill seekers who morph into vigilante groups with the propensity for violence (Beaver et al., 2008; Piacentico, Kotzalidis, del Casale et al., 2015). This finding was not sustained by other researchers (Bahsin, Storer, Berman et al., 1996; Yates et al., 1999). In the national situation, the youth appear to use bodybuilding as part of their health-seeking needs, although there are aspects of bodybuilding that may not contribute to overall good health, such as the injection of steroids for muscle mass. Those recruited into Political Party Vigilante Groups join through peer recommendation, Political Party affiliation, personal choice and direct employment by “strongmen and financiers” in the Political Parties. A more objective approach to reporting on, and understanding of bodybuilding and its association with vigilantism or election violence should be adopted. Political Party vigilantism appears to be an effect of systemic economic malaise and under-development, which ought to be addressed by central government through progressive investment in education and employment opportunities.
Speaker diarization algorithm based on CLR and BIC

The task of Speaker diarization is to group together speech segments uttered by the same sp-eaker. This paper presents an approach to speaker diarization based on a novel combination of Cross Log-likelihood Ratio (CLR) and standard Bayesian information criterion (BIC). Cross Log-likelihood Ratio pro-vides an inter-cluster distance measure,while BIC provides a proper stopping criterion for clustering. The method combines the advantage of these two methods and yields favorable performance in unsupervised speaker diarization. Experiment results show that the performance of the proposed approach based on combination of the CLR and BIC,is better than the approach only based on CLR clustering.
Household Split, Income, and Migrants’ Life Satisfaction: Social Problems Caused by Rapid Urbanization in China

Household split between rural and urban areas has become an important social issue in China’s urbanization process. This study analysed the influence of household split on migrants’ life satisfaction and the differences between inter- and intra-provincial migrants. Using the data of the 2014 China Migrants Dynamic Survey, we found that the life satisfaction of inter-provincial migrants was significantly lower than that of intra-provincial migrants. For inter-provincial migrants, the life satisfaction of those who moved to the city with underaged children was significantly lower than that of those who left their children in their hometown. Moreover, the life satisfaction of migrants who were concerned about childcare in the hometown was significantly lower than that of those who did not worry about it. Chinese migrants face a dilemma: bringing their family members to the city despite the lack of social welfare support or leaving them in the hometown worrying about family care. We also found that family income does not have a significant moderating effect on the decline in life satisfaction owing to concerns about childcare in the hometown. Future policy concerning China’s population should create external conditions for migrants to accomplish family reunion.
Efficient Algorithms for Mining Fuzzy Rules in Large Relational Databases

Mining association rules and sequential rules from large databases is an important task of data mining. Previous work is focused on definite and accurate concepts, which may not be concise and meaningful enough for human experts to easily obtain nontrivial knowledge from the rules discovered. The definition of fuzzy concepts is based on fuzzy set theory, which is especially useful when the discovered rules are presented to human experts for examination. In this paper, algorithms are presented for discovering fuzzy association rules and fuzzy sequential rules expressed by fuzzy concepts from large relational databases.
Rehabilitation Systems in Ambient Assisted Living Environments

This chapter reviews Ambient Assisted Living (AAL) in the context of movement-based rehabilitation. The authors analyse the need for AAL solutions and how they can overcome many of the drawbacks associated with traditional rehabilitation. They discuss the benefits and challenges of rehabilitation within the AAL paradigm and the well-known benefits that the telerehabilitation and telemedicine models have already established. The authors review the top ambient technologies in use today, detailing their advantages and shortcomings. The review focuses primarily on areas such as motion capture, serious games, and robotic rehabilitation. The authors carry out a structured search of two well-known databases to find the most recent advances and present the most interesting lines of research and development. Finally, the authors discuss the review findings and draw conclusions on the future of personalised rehabilitation within an AAL paradigm.
Frame synchronization for a channel with different data rates

In some communication links, different data rates are used at different times on the same channel and each is assigned a separate frame synchronization (sync) marker. The frame search is completed within a finite number of frame intervals once both phase-lock and bit-sync are achieved because the incoming data rate can change. The probability of failure to frame-lock, and an upper bound of probability of false frame-lock in a frame search mode are derived for a communication link with a 10/sup -5/ bit error probability. The probability of failure to detect data rate change and the probability of false detection of data rate change in a frame-lock mode are discussed. In a frame-lock mode, the probability of failure to detect data rate change was equal to 0.12*10/sup -6/ and the probability of false detection of data rate change was equal to 0.496*10/sup -11/. A frame-lock device can be used for the Space Station Freedom Ku-band return link. <<ETX>>
Semantic similarity computation of Baidu encyclopedia entries based on SimRank

The measurement of the semantic similarity using semi-structured data on Baidu encyclopedia was proposed.The encyclopedia entries and related entries were considered as two nodes of a directed graph,of which there was a link between two nodes.Then SimRank algorithm was used to calculate the semantic similarity of encyclopedia entries.Experimental results showed that the proposed measure significantly outperformed the traditional similarity measures,and might accurately reflect the semantic relationship between words.