Miracle-dz/UniFAR-FTUs huggingface dataset:
A modified technique for smart textural feature selection to extract retinal regions of interest using image pre-processing

To increase the efficiency of the laser coagulation surgery the problem of the most accurate segmentation of fundus images is especially relevant. Fundus image segmentation is carried out with high accuracy using effective features and the minimum number of parameters for segmentation of a single image fragment. This paper describes a modified technique for smart textural feature selection to extract retinal regions of interest using image preprocessing algorithms. Preprocessing algorithms significantly influence the selected features which provide a minimum error of object recognition. In addition image preprocessing algorithms provide a more precise object selection. The informativeness of the obtained feature space is studied using discriminant data analysis. The best fragmentation block size segmentation and feature sets provides the necessary accuracy to identify regions of interest. Those regions are determined by the analysis of the following 4 classes of fundus images: exudates, thick, thin vessels and healthy areas. The advantages and disadvantages of the considered preprocessing algorithms were identified.
Economic freedom and effects on economic growth: A time series analysis for Turkey

Abstract. Economic freedom represents personally free economic activity level. Thus such as Fraser Insitute or Heritage Foundation the level of economic freedom in Turkey for a compound period of before and after 1980 has been constituted originally. Freedom index referred to internationally measures of organization composed of different indicators. These indicators are derived from the point of view of market economy, liberal perspective. Level of economic freedom indicated by index values are to be analyzed econometrically with another economic indicators. Other variables are human capital, income per labor stemmed from MRW Model of economic growth, physical capital etc. Results of time series analysis with ARDL Model, Cointegration Methods found out significant relationship between economic freedom and growth. Findings are differently and specifically evaluated for Turkish economy and political economy. Keywords. Economic freedom, Economic growth, Turkish economic freedom index, ARDL bound test approach, Johansen cointegration analysis. JEL. F43, N10, O10.
Predicting targets in Multiple Object Tracking task

The aim of this thesis is to predict targets in a Multiple Object Tracking (MOT) task, in which subjects track multiple moving objects. We processed and analyzed data containing object and gaze position information from 1148 MOT trials completed by 20 subjects. We extracted multiple features from the raw data and designed a machine learning approach for the prediction of targets using neural networks and hidden Markov models. We assessed the performance of the models and features. The results of our experiments show that it is possible to train a machine learning model to predict targets with very high accuracy.
A COMBINED APPROACH TO PART-OF-SPEECH HOMONYMY RESOLUTION

The Russian language has an inflective structure and does not have a strict word order, which generates processing problems such as part-of-speech homonymy. The paper addresses this issue. The existing approaches to resolving the morphological homonymy problem can be divided into the following groups: rule-based approaches, statistical approaches, machine learning approaches, and combined methods. In the paper, we showed that each approach has its advantages and disadvantages; however, we can achieve a much higher precision of the algorithm by combining several approaches. The combined method based on neural networks gives better results than others (98% precision obtained). We used the following features: normalizing substitutions, grammatical and syntactic characteristics, vector representation of the word, and word forms. All the experiments were performed on the part of the National Corpus of the Russian Language with homonymy resolution. The analysis of the corpus revealed that the most frequent types of homonymy occurred between function words: a particle vs an interjection (14%), and a preposition vs an interjection (13.2%).
The Limits to Moral Erosion in Markets: Social Norms and the Replacement Excuse

This paper studies the impact of a key feature of competitive markets on moral behavior: the possibility that a competitor will step in and conclude the deal if a conscientious market actor forgoes a profitable business opportunity for ethical reasons. We study experimentally whether people employ the argument "if I don’t do it, someone else will" to justify taking a narrowly self-interested action. Our data reveal a clear pattern. Subjects do not employ the "replacement excuse" if a social norm exists that classifies the selfish action as immoral. But if no social norm exists, subjects are more inclined to take a selfish action in situations where another subject can otherwise take it. By demonstrating the importance of social norms of moral behavior for limiting the power of the replacement excuse, our paper informs the long-standing debate on the effect of markets on morals.
Scalable mobility support in future internet architectures

In this thesis, we present MobileNDN, a scalable design for producer mobility support in the Named Data Network (NDN) architecture. While the initial design of NDN provided support for consumer mobility automatically via its stateful forwarding plane, a solution for producer mobility was left unspecified. The mobility support scheme we propose decouples the tasks of 1) detecting whether data may exist on a mobile producer, and of 2) forwarding interest packets towards the mobile producer. First, we use NDNS to detect zones that may be served by mobile producers. Second, based on insights from MobilityFirst, we introduce a scalable global mapping service to locate mobile producers. The combination of these two components yields a mobility solution that allows NDN's forwarding to operate and scale in the face of mobile consumers and producers. Thesis Supervisor: Karen R. Sollins Title: Principal Research Scientist
Do Federal Deficits Motivate Regional Fiscal (Im)Balances? Evidence for the Spanish Case

This paper studies the vertical and horizontal interactions existing between federal and state governments in terms of public deficits. We estimate a fiscal reaction function for the Spanish regions over the period 1995-2010 paying special attention to the impact of federal fiscal stance on the state fiscal imbalances. Our results indicate that higher public deficits of the central government encourage bigger fiscal imbalances at state level. This vertical interaction is interpreted in the context of yardstick competition models. We also find a significant impact of fiscal decisions taken by governments at the same tier of decision on a specific state.
Network visualisations of knowledge assets: their value and user experiences for innovation development

This paper explores the value of network visualisations for presenting complex knowledge assets to executive decision-makers in order to develop culturally relevant insights for programme development. The value is first addressed with an analysis of network visualisation process called 'Ostinato', an operational context in relation to cognitive fit as a theoretical context. Then, value is explored with an analysis using taxonomy of interactive dynamics for visual analytics. Furthermore, evaluating the usability of visualisation is conducted in the context of the Parisian ecosystem, with board members using network visualisations to explore complex multi-layered knowledge about relationships among key executives, companies and financing organisations. All findings support the argument that value of knowledge assets in problem-solving performance depends on both format of the data and nature of the task. Furthermore, the findings support the importance of continual involvement and interaction between data analysts and decision-makers; they highlight the importance of considering knowledge assets as value drivers that can support knowledge-based innovation.
Surface Material Retrieval Using Weakly Paired Cross-Modal Learning

In this paper, we investigate the cross-modal material retrieval problem, which permits the user to submit a multimodal query including tactile and auditory modalities, and retrieve the image results of visual modalities. Since multiple significantly different modalities are involved in this process, we encounter more challenges compared with the existing cross-modal retrieval tasks. Our focus is to learn cross-modal representations when the modalities are significantly different and with minimal supervision. A novelty is that we establish a framework that deals with weakly paired multimodal fusion method for heterogenous tactile and auditory modalities and weakly paired cross-modal transfer for visual modality. A structured dictionary learning method with a low rank and common classifier is developed to obtain the modal-invariant representation. Finally, some cross-modal validations on publicly available data sets are performed to show the advantages of the proposed method. Note to Practitioners—Cross-modal retrieval is an important task for industrial intelligence. In this paper, we establish a framework to effectively solve the cross-modal material retrieval problem. In the developed framework, the user may submit a multimodal query including acceleration and sound about an object, and the system may return the most relevant retrieved images. Such a framework may find extensive applications in many fields, because it can be flexible to deal with a multiple-modal query and uses the minimal category label supervision without the need of strong sample pairing information between modalities. Compared with the previous material analysis systems, this paper goes beyond previously proposed surface material classification approaches as it returns an ordered list of perceptually similar surface materials for a query.
First experimental results of a cryogenic stopping cell with short-lived, heavy uranium fragments produced at 1000 MeV/u

A cryogenic stopping cell (CSC) has been commissioned with 238U projectile fragments produced at 1000 MeV/u. The spatial isotopic separation in flight was performed with the FRS applying a monoenergetic degrader. For the first time, a stopping cell was operated with exotic nuclei at cryogenic temperatures (70 to 100 K). A helium stopping gas density of up to was used, about two times higher than reached before for a stopping cell with RF ion repelling structures. An overall efficiency of up to 15%, a combined ion survival and extraction efficiency of about 50%, and extraction times of 24 ms were achieved for heavy α-decaying uranium fragments. Mass spectrometry with a multiple-reflection time-of-flight mass spectrometer has demonstrated the excellent cleanliness of the CSC. This setup has opened a new field for the spectroscopy of short-lived nuclei.
Estimation of Sequential Search Model

We propose a new likelihood-based estimation method for the sequential search model. By allowing search costs to be heterogeneous across consumers and products, we can directly compute the joint probability of the search sequence and the purchase decision when consumers are searching for the idiosyncratic preference shocks in their utility functions. Under this procedure, one recursively makes random draws for each dimension that requires numerical integration to simulate the probabilities associated with the purchase decision and the search sequence under the sequential search algorithm. We then present details from an extensive simulation study that compares the proposed approach with existing estimation methods recently used for sequential search model estimation, viz., the kernel-smoothed frequency simulator (KSFS) and the crude frequency simulator (CFS). In the empirical application, we apply the proposed method to the Expedia dataset from Kaggle which has previously been analyzed using the KSFS estimator and the assumption of homogeneous search costs. We demonstrate that the proposed method has a better predictive performance associated with differences in the estimated effects of various drivers of clicks and purchases, and highlight the importance of the heterogeneous search costs assumption even when KSFS is used to estimate the sequential search model. Lastly, from a managerial perspective, we show that sorting products by their expected utilities can enhance consumer welfare and increase the number of transactions.
Capability requirements oriented weapon system of systems portfolio planning model and algorithm

Capabilities based planning(CBP) has become the mainstream strategy gradually to develop weapon system of systems(WSoS),but it lacks quantitative model and algorithm to implement the practical process of CBP in recently research.To solve this problem,the modeling and solution are investigated on Capability based WSoS Portfolio Planning,a quantitative description of capability requirements is proposed firstly,a portfolio planning model of WSoS is constructed based on combinatorial optimization and norm ideal point method secondly,and then a solving algorithm is proposed based on differential evolution,the effectiveness of the proposed model and algorithm is demonstrated by a portfolio planning example of intelligence,surveillance,reconnaissance WSoS.The model and algorithm can support the decision making on implementing CBP to plan and demonstrate WSoS.
Econometric Analysis of Influence of Monetary Policy on Macroeconomic Aggregates in Indian Economy

This study attempts to examine the relationship between macroeconomic indicators of India and its monetary policy over the period 1980 to 2017. The paper studies the correlation between GPD, Balance of Payments, Inflation, Balance budget, Public and such independent variables as international reserves, money and quasi money, interest rate and reserve ratio with the help the linear regression using OLS and ANOVA test in Excel. From the analysis, it is clear that GDP, Public Debt and Inflation have the strongest dependence and significance in relation to monetary indicators.
Digital Signal Modulation Recognition Equipment Based on High-order Cumulants

For several reasons, modulation recognition is extremely important in wireless communication. In this paper, a common method, based on fourth-order and sixth-order cumulants, is proposed to recognize the modulation of digital signals in the device. Automatic modulation recognition is a fundamental step to perform the classification of different modulation formats. It is very important both in military and civil applications. Different signal transmission requires different modulation. These modulation makes user convenient to work in such communication field. This device is mainly used to recognize the modulation of the digital signals automatically. The function of the device is mainly used to distinguish different digital signals.
2014-2017 Update to Graham Greenleaf's Asian Data Privacy Laws - Trade and Human Rights Perspectives

In July 2017 I published on SSRN an update from mid-2014 to mid-2017 of my Asian Data Privacy Laws – Trade and Human Rights Perspectives (Oxford University Press, 2014), to accompany the publication of the paperback edition in July 2017. This further update aims to cover developments to 31 October 2018. The content of this update first covers International Agreements affecting privacy which are of relevance to Asia. Summaries follow of developments in the fifteen months prior to November 2018 from any of the 26 jurisdictions in Asia, considered region-by-region (North-East Asia; South-East Asia; and South Asia). This period has been a very significant one for Asian data privacy laws, with the most important developments being: * The anti-privacy provisions in the 'new TPP', the Comprehensive and Progressive Trans-Pacific Partnership (CPTPP) free trade agreement; * The initial impact of the EU's General Data Protection Regulation (GDPR) on Asian jurisdictions; * The draft adequacy decision concerning japan, by the European Commission; * Amendments to Korea's 'Network Act', and their significance for Korea's adequacy applicaition to the EU; * Strongly GDPR-influenced draft Bills in both Thailand and Indonesia; * Vietnam's Cybersecurity Law and its strong data localisation provisions; * The Indian Supreme Court's unanimous decision in Puttaswamy v Union of India that India has a fundamental constitutional right of privacy, and its decision by majority a year later that India's biometric ID system, the Aadhaar, while constitutionally capable of validity, was implemented by legislation which is in part invalid; * The Report by the India's Srikrishna Committee, and its GDPR and Chinese influenced draft data protection Bill.
Multiplicativity Factors for Function Norms

Let (T, Ω, m) be a measure space; let ρ be a function norm on = (T, Ω, m), the algebra of measurable functions on T; and let Lρ be the space {f ∈ : ρ(f) 0 a multiplicativity factor for ρ if ρ(fg) ≤ μρ(f) ρ(g) for all f, g ∈ Lρ. Similarly, λ > 0 is a quadrativity factor if ρ(f2) ≤ λρ(f)2 for all f. The main purpose of this paper is to give conditions under which Lρ, is indeed an algebra, and to obtain in this case the best (least) multiplicativity and quadrativity factors for ρ. The first of our two principal results is that if ρ is σ-subadditive, then Lρ is an algebra if and only if Lρ is contained in L∞. Our second main result is that if (T, Ω, m) is free of infinite atoms, ρ is σ-subadditive and saturated, and Lρ, is an algebra, then the multiplicativity and quadrativity factors for ρ coincide, and the best such factor is determined by sup{||f||∞: f ∈ Lρ, ρ(f) ≤ 1}.
An Object Tracking Method Based on the Coordinate-Locating of SIFT Algorithm

The SIFT (scale invariant feature transform) algorithm has been successfully used in the image matching field. In this paper, a simplified SIFT algorithm is designed. The number of layers in the Gaussian pyramid is reduced. When it is comparing the keypoints, it uses an outspreading method. The new method can reduce the comparison time and matching time. Although the new algorithm (C-SIFT algorithm) has less matching accuracy than the SIFT algorithm, it adopts a distortion detection method to abandon the wrong matching. Then it uses the coordinate displacement to determine the tracking position. Experimental results show that C-SIFT algorithm can perform steadily and timely.
Characterizing Driver Trust in Vehicle Control Algorithm Parameters

Human factors research in vehicle automation has focused on user interfaces such as performance feedback through visual and auditory displays (Blanco et al., 2015). Another approach is to use vehicle dynamics and vibrations as communicative tools for guiding attention (e.g., Morando, Victor, & Dozza, 2016; Walker, Stanton, & Young, 2006; Wiese & Lee, 2007). In our previous study (Price, Venkatraman, Gibson, Lee, & Mutlu, 2016), we showed that the steering wheel deadband, or lateral movement of the vehicle while maintaining lane position, was negatively associated with trust—more lateral movement led to less trust in the algorithm. The present study extends these findings by using Bayesian statistical methods with new control algorithm data. Although the inclusion of additional algorithm characteristics did not improve the trust model, the use of Bayesian statistical methods provides a useful tool to incorporate prior knowledge into an analysis.
Keypoint-based passive method for image manipulation detection

Abstract Due to the availability of media editing software, the authenticity and reliability of digital images are important. Region manipulation is a simple and effective method for digital image forgeries. Hence, the potential to identify the image manipulation is current research issue these days and copy-move forgery detection (CMFD) is a main domain in image authentication. In copy-move forgery, one region is simply copied and pasted over other regions in the same image for manipulating the image. In this paper, we have proposed a method based on Harris corner and Adaptive non-maximal Suppression (ANMS) for manipulation detection in an image. Initially, the input image is taken and then Harris corner detection algorithm is used to detect the interest points and ANMS is adopted to control the number of Harris points in an image. This gives a proper number of interest points for the different size of images and gives the assurance for finding the manipulated region in manageable time. For each extracted interest points we calculate the descriptors using SIFT then for the matching process of obtained descriptors, we use the outlier rejection with the nearest neighbour. Here, RANSAC is used to find the best set of matches to identify the manipulated regions. Experimental results show the robustness against different transformation and post-processing operations.
Volatility Timing under Low-Volatility Strategy

The authors show that the slope of the volatility decile portfolio’s return profile contains valuable information that can be used to time volatility under different market conditions in the United States. During good (bad) market conditions, the high- (low-) volatility portfolio produces the highest return. The authors proceed to devise a volatility timing strategy based on statistical tests on the slope of the volatility decile portfolio’s return profile. Volatility timing is achieved by being aggressive during strong growth periods and conservative during market downturns. Superior performance is obtained, with an additional return of 4.1% observed in the volatility timing strategy, resulting in a fivefold improvement on accumulated wealth, along with statistically significant improvement in the Sortini ratio and the information ratio. The authors also demonstrate that stocks in the high-volatility portfolio are more strongly correlated compared to stocks in the low-volatility portfolio. Hence, the profitability of the volatility timing strategy can be attributed to successfully holding a diversified portfolio during bear markets and holding a concentrated growth portfolio during bull markets. Key Findings ▪ The return profile of the volatility decile portfolio is time-varying. Its slope contains vital information on market condition—high-volatility portfolio outperforms low-volatility portfolio during good market condition, but underperforms during bad market condition. Since market regime and asset price behaviors are persistent, the slope parameter can be used to time volatility exposure. ▪ Holding the low-volatility portfolio benefits from the higher risk-adjusted return during general market condition. However, when the slope parameter is positive and statistically significant, it is optimal to hold the high-volatility portfolio for the subsequent period. This will ride on the higher return of high-volatility portfolio during strong growth periods. This leads to higher return and increased volatility, but both Sortini ratio and Information ratio exhibit statistically significant improvement. ▪ Stocks in the low-volatility portfolio are less correlated than stocks in the high-volatility portfolio. The outperformance of the volatility timing strategy formulated in this article can be attributed to holding a concentrated growth portfolio during good market conditions, and holding a diversified portfolio during bad market conditions, thus connecting the literature on low-volatility portfolio with studies on correlation structure and diversification.