Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Rights Management for Photocopiers

We introduce a system to automatically manage photocopies made from copyrighted printed materials. The system monitors photocopiers to detect the copying of pages from copyrighted publications. Such activity is tallied for billing purposes. Access rights to the materials can be verified to prevent printing. Digital images of the copied pages are checked against a database of copyrighted pages. To preserve the privacy of the copying of non-copyright materials, only digital fingerprints are submitted to the image matching service. A problem with such systems is creation of the database of copyright pages. To facilitate this, our system maintains statistics of clusters of similar unknown page images along with copy sequence. Once such a cluster has grown to a sufficient size, a human inspector can determine whether those page sequences are copyrighted. The system has been tested with 100,000s of pages from conference proceedings and with millions of randomly generated pages. Retrieval accuracy has been around 99% even with copies of copies or double-page copies.
Developing Causal Relationships for CPFR Index: a System Dynamics Simulation Approach

Collaborative planning, forecasting and replenishment (CPFR) is one of the collaborative strategies in supply chain management that aims to coordinate the diverse processes of supply chain management. Despite being identified as playing an important role in supply chain performance improvement, the dynamic interactions among its subsystems have not been explored in previous research. This research aims to identify the interactions among effective enablers and the potential impact of each enabler on successful CPFR implementation through the development of a structural model and system dynamics simulation modelling. To reach this goal, significant enablers and results reported in previous research have been explored. The causal relationships among different variables have been analysed using through the application of system dynamics and stock and flow diagrams. A dynamic CPFR model is proposed based on a number of assumptions. The resulting causal loop helps the reader to better understand and learn the dynamic interactions of CPFR subsystems. 
Learning, Inflation Reduction and Optimal Monetary Policy

In this paper we analyze disinflation in two environments.One in which the central bank has perfect knowledge, in the sense that it understands and observes the process by which private sector inflation expectations are generated, and one in which the central bank has to learn the private sector inflation forecasting rule.Here, the learning scheme we investigate is that of least-squares learning (recursive OLS) using the Kalman filter.With imperfect knowledge, results depend on the learning scheme that is employed.A novel feature of the passive learning policy - compared to the central bank s disinflation policy under perfect knowledge - is that the degree of monetary accommodation (the extent to which the central bank accommodates private sector inflation expectations) is no longer constant across the disinflation, but becomes state-dependent.This means that the central bank's behaviour changes during the disinflation as it collects more information.
Efficient Removal of Inconsistencies in Large Multi-Scan Point Clouds

When large scale structures are to be digitized using laser scanning, usually multiple scans have to be registered and merged to cover the whole scene. During the scanning process movement in the scene and equipment standing around – which is not always avoidable – may induce artifacts. Since the scans often overlap considerably another scan of the same area might be artifact-free. In this paper we describe an approach to find these "temporal" artifacts (so-called, because they only appear during one single scan) based on shadow mapping, which makes it implementable as a shader program and therefore very fast. The effectiveness of the approach is demonstrated with the help of large-scale real-world data.
Forecasting using principal components from many predictors

This paper studies macroeconomic forecasting with many predictors, and primarily uses approximate dynamic factor models, in which a small number of latent factors summarize the information of predictors. Principal component analysis has been brought into widely use to estimate factors, and four kind of principal component (PC) estimators are examined in details, which are standard PCs, weighted PCs, generalized PCs and dynamic PCs. Furthermore, targeted PC estimation is proposed as another type of estimated factors in the paper, in order to take into account the purpose of forecasting variables of interest, which is accomplished by ordering PCs with their importance to prediction and selecting a handful targeted PCs. Then all these estimates are compared in the context of forecasting macroeconomic series at various time horizons, meanwhile other many-predictors forecasting methods are considered into the evaluation.The empirical data consists of 132 U.S. monthly time series available from 1969:1 to 2003:12. The results suggest that one or two estimated factors can valuably summarize the information from many predictors; however, forecasts are different and relative to variables of study. ∗Postal address: Mathematical Statistics, Stockholm University, SE-106 91, Sweden. E-mail: ypang@math.su. se. Supervisor: Martin Sköld. Forecasting Using Principal Components from Many Predictors Preface This thesis constitutes a Master’s project for the degree of Master in Financial Mathematics and Finance, Department of Mathematics, Stockholm University. Acknowledgement I would like to give my sincere appreciation to my supervisor, Martin Sköld, for his great guidance and support. I also would like to thank my dear family for their unconditional love. Forecasting Using Principal Components from Many Predictors
Don't slip on the ICO - a Taxonomy for a Blockchain-Enabled Form of Crowdfunding

Blockchain is rapidly evolving and there is an increasing interest in the technology in both practice and academia. Recently, a blockchain use case called Initial Coin Offering (ICO) draws a lot of attention. ICO is a novel form of crowdfunding that utilizes blockchain tokens to allow for truly peer-to-peer investments. Although, more than 4.5 billion USD have been invested via ICOs, the phenomenon is poorly understood. Scientific research lacks a structured classification of ICOs to provide further insights into their characteristics. We bridge this gap by developing a taxonomy based on real-world ICO cases, related literature, and expert interviews. Further, we derive and discuss prevailing ICO archetypes. Our findings contribute to theory development in the field of ICOs by enriching the descriptive knowledge, identifying design options, deriving ICO archetypes, and laying the foundation for further research. Additionally, our research provides several benefits for practitioners. Our proposed taxonomy illustrates that there is no one-size-fits-all model of ICOs and might support the decisionmaking process of start-ups, investors and regulators. The proposed ICO archetypes indicate how common ICOs are designed and thus might serves as best practices. Finally, our analysis indicates that ICOs represent a valid alternative to traditional crowdfunding approaches.
The Effects of Organ-based Tube Current Modulation on Radiation Dose and Image Quality in Computed Tomography Imaging

THE EFFECTS OF ORGAN-BASED TUBE CURRENT MODULATION ON RADIATION DOSE AND IMAGE QUALITY IN COMPUTED TOMOGRAPHY IMAGING Diksha Gandhi Marquette University, 2014 The purpose of this thesis was to quantify dose and noise performance of organ-dosebased tube current modulation (ODM) through experimental studies with an anthropomorphic phantom and simulations with a voxelized phantom library. Tube current modulation is a dose reduction technique that modulates radiation dose in angular and/or slice directions based on patient attenuation. ODM technique proposed by GE Healthcare further reduces tube current for anterior source positions, without increasing current for posterior positions. Axial CT scans at 120 kV were performed on head and chest phantoms (Rando Alderson Research Laboratories, Stanford, CA) on an ODM-equipped scanner (Optima CT660, GE Healthcare, Chalfont St Giles, England). Dosimeters quantified dose to breast, lung, heart, spine, eye lens and brain regions (mobile MOSFET Dosimetry System, Best Medical, Ottawa, Canada) for ODM, AutomA (z-axis modulation), and SmartmA (angular and z-axis modulation) settings. Noise standard deviation was calculated in brain and chest regions of reconstructed images. To study a variety of patient sizes, Monte Carlo dose simulations, validated with experimental data, were performed on voxelized head and chest phantoms. Experimental studies on anthropomorphic chest and head phantoms demonstrated reduction in dose at all dosimeter locations with respect to SmartmA, with dose changes of 31.3% (breast), -20.7% (lung), -24.4% (heart), -5.9% (spine), -18.9% (eye), and -10.1% (brain). Simulation studies using voxelized phantoms indicated average dose changes of -33.4% (breast), -20.2% (lung), -18.6% (spine), -20.0% (eye) and -7.2% (brain). ODM reduced dose to the brain and lung tissues, however these tissues would experience up to 15.2% and 13.1% dose increase respectively at noise standard deviation equal to SmartmA. ODM reduced dose to the eye lens in 22 of 28 phantoms (-1.2% to -12.4%), had no change in dose for one phantom, and increased dose for four phantoms (0.7% to 2.3% ) with respect to SmartmA at equal noise standard deviation. All phantoms demonstrated breast dose reduction (-2.1% to -27.6%) at equal noise standard deviation. Experimental and simulation studies over a range of patient sizes indicate that ODM has the potential to reduce dose to radiosensitive organs by 5 38% with a limited increase in image noise.
What the Novel H1N1 Influenza Outbreak Can Teach us about Influenza as a Bioterrorism Weapon

While we often see Influenza infections as part of our daily lives during winter in the United States, it is a virus with significant potential for manipulation into an agent that may be used in the framework of Bioterrorism. The Novel H1N1 outbreak of 2009 highlighted what our response is to such an unsuspected outbreak both on a national and global scale. The responses to this outbreak allowed us to avert significant damage from the Pandemic, but retrospective analysis suggests that if the outbreak had been more virulent, greater impact would have been felt on a global scale. This short review attempts to briefly summarize our response to the Novel H1N1 outbreak in the United States and to apply the knowledge gleaned from this analysis to a hypothetical Bioterrorism attack that employs a modified Influenza virus, an event that is unlikely but not impossible.
Investigations on herbicide resistance in Apera spica-venti populations

In this study, the results of two-year investigations on herbicide resistance in silky bent grass (Apera spica-venti) populations are presented. Greenhouse assays were conducted with different herbicides on more than 250 silky bent grass populations from Central and Eastern European agricultural fields where herbicides had not performed satisfactorily. Results revealed that more than 60% of suspected populations were resistant to acetolactate synthase (ALS)inhibitors. In contrast to this, resistance to acetyl-CoA carboxylase (ACCase)-inhibitors could be observed only sporadically and no resistance to photosystem II (PSII)-inhibitors could be detected. Dose-response assays conducted in the greenhouse on field-selected populations revealed resistance factors at the ED50 and ED90 ranging from 11 to 142 after treatment with flupyrsulfuron-methyl, from 2 to 15 after treatment with mesosulfuron+iodosulfuron and from 4 to 6 after treatment with fenoxaprop-P-ethyl, thus confirming a prevalent resistance to ALSinhibiting herbicides in silky bent grass. Within further greenhouse experiments, percent canopy cover after treatment with sulfosulfuron and fenoxaprop-P-ethyl was determined in sensitive and resistant populations by using digital image analysis. A significant effect of herbicide dose on canopy cover was observed in sensitive plants 7 and 15 days after treatment with sulfosulfuron and in all populations after treatment with fenoxaprop-P-ethyl. Statistical analysis revealed significant correlations between canopy cover and plant dry weight in all populations. This work, through the presentation of previously established and newly-developed methods, provides weed scientists with a broad range of practical tools for a reliable diagnosis of herbicide resistance in weed populations.
The effect of enzymatic hydrolysis on amino acids composition of Persian sturgeon (Acipenser persicus) viscera protein hydrolysate

Protein hydrolysate was prepared from visceral waste proteins of Persian sturgeon (Acipenser persicus), an Iranian major sturgeon. Hydrolysis was performed at 55 ° C, 205 min, pH 8.5 by 0.1 AU/g protein E/S Alcalase 2.4L. Protein content for hydrolysate was 65.82%, and lipid content was 0.18%. The highest degree of hydrolysis was observed in conditions of 55°C after 205 min. (46.13%) (P<0.05). The results showed that the amino acid profiles of the Persian sturgeon viscera hydrolysates were generally higher in essential amino acid profiles compared with the suggested pattern of requirement by FAO/WHO for adult humans and could fulfill this requirement. Key word: Enzymatic hydrolysis, Sturgeon Protein hydrolysate, amino acid profile, degree of hydrolysis Introduction Every year over 132 million tons of fish are harvested, of which 29.5% is converted into fish meal (FAO, 2006). Possibly more than 50% of the remaining fish tissue is considered to be non-edible waste material. With dramatically increasing world population and a world catch of fish more than 100 million tons/year, there is obviously an increased need utilize our sea resources with more intelligence and foresight. By applying enzyme technology for protein recovery in fish processing, it may be possible to produce a broad spectrum of food ingredients or industrial products for a wide range of applications. This would utilize both fisheries byproducts and secondary raw materials and, in addition, underutilized species that would otherwise be discarded. Fish viscera are one of the most important byproducts, which rich source of protein and polyunsaturated lipids with low storage stability if not frozen or otherwise preserved. Enzymatic modification of proteins using selected proteolytic enzyme preparations to cleave specific peptide bonds is widely used in the food industry (Kristinsson and Rasco, 2000a). Generally, Alcalase 2.4 L-assisted reactions have been repeatedly favoured for fish hydrolysis due to the high degree of hydrolysis that can be achieved in a relatively short time under moderate pH conditions compared to neutral or acidic enzymes (Hoyle and Merritt, 1994; Shahidi et al., 1995; Kristinsson and Rasco, 2000a, b; Bhaskar et al., 2008). Hydrolyzing protein can also improve intestinal absorption (Kristinsson and Rasco, 2000a), and be used as a source of peptides, such as peptone, for ingredient in microbial growth media (Gildberg et al., 1989). Persian sturgeon is one of the most important sturgeon fishes on the Iranian coast of the Caspian Sea for both meat and caviar production. Sturgeon processing waste is usually discarded without any recovery except for the swim bladder. Sturgeon viscera could be a rich source of protein which can be used as
Monitoring of Soybean Oil Transesterification by Spectrofluorimetry and Multivariate Calibration

An analytical procedure was developed to monitor the transesterification of soybean oil using spectrofluorimetry and Partial Least Squares (PLS) regression. Excitation-Emission Matrix (EEM) fluorescence spectroscopy derived from 12 standard mixtures of triolein and ethyl oleate were used in the construction of a PLS model using the fluorescent spectra as independent variables and the values of the methyl oleate % (w/w) as dependent variables. The model presented a high correlation (0.99178) between values determined by the reference method (ATR-FTIR) and values predicted by the proposed method. The coefficient of determination (R2) for the curve was close to a value of 1 (0.9836), which indicated the strength in the association between the observed data for the two variables and the efficiency of the model to perform the analysis. Therefore, the association between spectrofluorimetry and PLS was shown to be a fast and accurate method for monitoring soybean oil transesterification.
Vegetation role in controlling the ecoenvironmental conditions for sustainable urban environments: a comparison of Beijing and Islamabad

Abstract. The rapid increase in urbanization due to population growth leads to the degradation of vegetation in major cities. This study investigated the spatial patterns of the ecoenvironmental conditions of inhabitants of two distinct Asian capital cities, Beijing of China and Islamabad of Pakistan, by utilizing Earth observation data products. The significance of urban vegetation for the cooling effect was studied in local climate zones, i.e., urban, suburban, and rural areas within 1-km2 quantiles. Landsat-8 (OLI) and Gaofen-1 satellite imagery were used to assess vegetation cover and land surface temperature, while population datasets were used to evaluate environmental impact. Comparatively, a higher cooling effect of vegetation presence was observed in rural and suburban zones of Beijing as compared to Islamabad, while the urban zone of Islamabad was found comparatively cooler than Beijing’s urban zone. The urban thermal field variance index calculated from satellite imagery was ranked into the ecological evaluation index. The worst ecoenvironmental conditions were found in urban zones of both cities where the fraction of vegetation is very low. Meanwhile, this condition is more serious in Beijing, as more than 90% of the total population is living under the worst ecoenvironment conditions, while only 7% of the population is enjoying comfortable conditions. Ecoenvironmental conditions of Islamabad are comparatively better than Beijing where ∼61% of the total population live under the worst ecoenvironmental conditions, and ∼24% are living under good conditions. Thus, Islamabad at this early growth stage can learn from Beijing’s ecoenvironmental conditions to improve the quality of living by controlling the associated factors in the future.
Movie Recommender System : Movies 4 u

: Many of the largest E-commerce Web sites are implementing recommender systems to help their customers find which products to purchase based on filtering techniques. Recommender systems help E-commerce sites to increase their sales. A movie recommendation system named MOVREC, based on collaborative filtering approach makes use of the information provided by users, analyzes them and then recommends the movie that is best suited to the user at that time using k-means algorithm. In this paper we study techniques i.e collaborative filtering recommender systems to learn about new users and their interests. These technique consist use of information theory to pick out the items that will give the most value to the recommender system, aggregate strategy to select the items the user is most likely to have an opinion about, and personalized techniques that predict which items a user will have an opinion about. This knowledge will empower researchers and serve as a road map to improve the state of the art recommendation techniques.
Prescriptive Analytics for MEC Orchestration

Orchestrating network and computing resources in Mobile Edge Computing (MEC) is an important item in the networking research agenda. In this paper, we propose a novel algorithmic approach to solve the problem of dynamically assigning base stations to MEC facilities, while taking into consideration multiple time-periods, and computing load switching and access latency costs. In particular, leveraging on an existing state of the art on mobile data analytics, we propose a methodology to integrate arbitrary time-period aggregation methods into a network optimization framework. We notably apply simple consecutive time period aggregation and agglomerative hierarchical clustering. Even if the aggregation and optimization methods represent techniques which are different in nature, and whose aim is partially overlapping, we show that they can be integrated in an efficient way. By simulation on real mobile cellular datasets, we show that, thanks to the clustering, we can scale with the number of time-periods considered, that our approach largely outperforms the case without time-period aggregations in terms of MEC access latency, and at which extent the use of clustering and time aggregation affects computing time and solution quality.
Stability and change in burnout: A 10‐year follow‐up study among primary care physicians

The aim of the present study is (1) to investigate the impact of patient demands on primary care physicians' burnout and (2) to examine the stability and change of burnout across time. Participants were drawn from the official Dutch registration system for primary health care, and longitudinal panel data (n = 165) from three waves with a 5-year time interval were used. They filled in the Maslach Burnout Inventory (MBI) and a validated scale for the assessment of patient demands. The results of various stability and change models that were tested using structural equation modelling (SEM) indicated that demanding patient contacts lead to increased burnout among physicians. In addition, the findings suggested that about one quarter of the variance in physician's actual burnout levels across one decade is accounted for by a stable component, whereas about three quarters is accounted for by a change component. Hence, physician burnout seems to be a rather chronic condition that may be aggravated by exposure to demanding patients.
Lessons learned from the birth and evolution of the EduFinland virtual community for educators

Educators around the world are experimenting with the possibilities virtual three dimensional worlds have for education and learning how to use these new environments efficiently. Virtual worlds have the potential to bring some new added value to education and educators can use them to create something that is not possible to do or show in traditional classrooms. Although a lot have been learned about virtual worlds and their potential, a lot more has to be learned before virtual worlds can become an integrated part of education on various levels. The article looks back at the birth and growth of the EduFinland community in the virtual world of Second Life and discusses lessons learned so far during the years of its existence. https://doi.org/10.34105/j.kmel. 2012.04. 008