Miracle-dz/UniFAR-FTUs huggingface dataset:
Grafting of Cellulose Based Materials : A Review

Cellulose is the most abundant naturally occurring polymer which is biodegradable and, thus acceptable from the environmental point of view. The polymeric material with desired properties is a current need of the society. However to reach the required application properties, such as hydrophobicity, adhesivity, selectivity, drug delivery, wettability and thermo-sensitivity etc., graft copolymerization of suitable monomer is an indispensable technique for cellulose modification. Graft co-polymerization of cellulose and cellulose derivatives has received much attention recently. In principle graft co-polymerization initiated by chemical treatment, photo-irradiation, high energy radiation technique etc. is documented in this chapter. Several prime controlling factors such as nature of backbone, effect of monomer, solvent, initiator etc. on grafting are also
Thank you in advance for reading, and for your feedback! Best, Diana 1 Revenue Generation in New States and Other Transitional Contexts

Not too much to read from me this week! This began as a comprehensive overhaul of my prospectus, and now has been pared back to the most basic elements of my “puzzle” and theory. (It’s never a good sign when you delete more from the document than you add in any given sitting.) I would appreciate your feedback on the motivation, initial theoretical insight, and potential steps toward formally modeling this. Also, I would love to hear if you think the large-N data sources I cite at the end will indeed be helpful when it comes to empirical applications. (I am sorry I haven’t included much on my plan for the “small-N” case studies yet. This will be wrapped into grant proposals for fieldwork that I am working on separately.) Thank you in advance for reading, and for your feedback! Best, Diana
Social Fragmentation , Electoral Competition and Public Goods Provision

In this paper we show that social fragmentation can trigger increased electoral competition and improved provision of public goods. We test this using large-scale data on family networks from over 20 million individuals in 15,000 villages of the Philippines. We take advantage of naming conventions to assess intermarriage links between families and use community detection network algorithms to identify the relevant clans in all of those villages. We show that there is more public goods provision and political competition in villages with more fragmented social networks, a result that is robust to controlling for a large number of village characteristics and to alternative estimation techniques. Using original survey data collected in 284 villages after the 2013 local elections, we also show that fragmented villages are associated with a broader distribution of political influence. *A previous version of this paper circulated under the title Village Social Network Structures and Electoral Competition. We thank Nick Eubank, James Fowler, Yana Gorokhovskaia, Steph Haggard, Alex Hughes, Franziska Keller, and Sarah Shair-Rosenfeld for helpful suggestions. We are also grateful to seminar and workshop participants at ASOG, Oslo, UVA, UBC, UCSD, UPSE and Warwick for feedback. All remaining errors are ours. †University of British Columbia: cesi.cruz@ubc.ca ‡University of Oxford: julien.labonne@bsg.ox. ac.uk §New York University: pablo.querubin@nyu.edu The campaign (.) is the culmination of a contest to see who makes best use of the social structure. Mary Hollsteiner, The Dynamics of Power
Growth Curve Modeling: An Alternative Approach to Estimating Patterns of Population Change

Working from the formal model of population growth, Pt = P0e, we use population change in the U.S. Great Plains between 1900 and 1930 to demonstrate the efficiency of an alternative approach to estimating population change, r: growth curve modeling (GCM). Through this method, the researcher treats processes of change as a trajectory rather than a series of panels, resulting in a more efficacious and parsimonious analysis. Three general aspects of population growth can be addressed through linear and non-linear GCM techniques, namely (1) establishing a mean growth rate that represents the general pattern of change across all units, (2) estimating the degree of local level variation around the average rate, and (3) analyzing the relative contribution of factors influencing this variation. Our findings reveal that the non-linear cubic model most appropriately suits the early years of development on the Great Plains, indicating that the rate of population change varied over the period with rapid growth at the outset followed by a decreasing rate of growth in the middle decade and an accelerating rate during the later portion. In addition, we observed considerable variation around the average growth rate, and attribute much of this to county settlement date, urban proximity, and economic base, each to a varying extent across the different models.
Based on Support Vector Regression for emotion recognition using physiological signals

Facial expression are widely used for emotion recognition. Facial expressions may be expressed differently by different people subjectively, inaccurate results are unavoidable. Nevertheless, physiological reactions are non-autonomic nerves in physiology. The physiological reactions and the corresponding signals are hardly to control while emotions are excited. Therefore, an emotion recognition system with consideration of physiological signals is proposed in this paper. A specific designed mood induction experiment is performed to collect physiological signals of subjects. Five biosensors including electrocardiogram, respiration, galvanic skin responses (GSR), blood volume pulse, and pulse are used. Then a Support Vector Regression (SVR) is used to train three regression curves of three emotions (sad, fear, and pleasure). Experimental results show that the proposed method based on SVR emotion recognition has a good performance in accuracy.
Feedback Based Load Balancing, Deflection Routing and Admission Control in OBS Networks

The Optical Burst Switching (OBS) paradigm allows statistical multiplexing directly at the optical layer. Thus, OBS networks are suited to carry traffic demands varying in either the short or long term. Due to the lack of buffering, burst contention due to short term variations can only be mitigated through deflection routing. For longer term variations, higher order mechanisms such as dynamic flow-balancing or flow shaping are generally proposed. In this paper, a unified scheme, based on a feedback mechanism combined with deflection routing and admission control is introduced to handle all types of traffic variations. The use of only one single scheme simplifies the architecture of OBS networks and enhances its flexibility. The validity of our technique is supported by simulation results.
Have we overestimated the value of ROC analysis under varying class distributions

We counsel caution in the application of ROC analysis for prediction of classifler accuracy under varying class distributions. We argue that it is only rea- sonable to expect ROC analysis to provide accurate error prediction under varying class distributions if the attributes are causally dependent upon the classes and the classes do not contain causally relevant subclasses whose frequency may vary at difierent rates.
A projective approach to nonnegative matrix factorization

In data science and machine learning, the method of nonnegative matrix factorization (NMF) is a powerful tool that enjoys great popularity. Depending on the concrete application, there exist several subclasses each of which performs a NMF under certain constraints. Consider a given square matrix $A$. The symmetric NMF aims for a nonnegative low-rank approximation $A\approx XX^T$ to $A$, where $X$ is entrywise nonnegative and of given order. Considering a rectangular input matrix $A$, the general NMF again aims for a nonnegative low-rank approximation to $A$ which is now of the type $A\approx XY$ for entrywise nonnegative matrices $X,Y$ of given order. In this paper, we introduce a new heuristic method to tackle the exact nonnegative matrix factorization problem (of type $A=XY$), based on projection approaches to solve a certain feasibility problem.
Towards Explicit Semantic Features using Thresholded Independent Component Analysis

Latent semantic analysis (LSA) can be used to create an implicit semantic vectorial representation for words. Independent component analysis (ICA) can be derived as an extension to LSA that rotates the latent semantic space so that it becomes explicit, that is, the features correspond more with those resulting from human cognitive activity. This enables nonlinear filtering of the features, such as hard thresholding that creates a sparse word representation where only a subset of the features is required to represent each word successfully. We demonstrate this with semantic multiple choice vocabulary tests. The experiments are conducted in English, Finnish and Swedish.
Chronic Arsenic Poisoning Probably Caused by Arsenic-Based Pesticides: Findings from an Investigation Study of a Household

In addition to naturally occurring arsenic, man-made arsenic-based compounds are other sources of arsenic exposure. In 2013, our group identified 12 suspected arsenicosis patients in a household (32 living members). Of them, eight members were diagnosed with skin cancer. Interestingly, all of these patients had lived in the household prior to 1989. An investigation revealed that approximately 2 tons of arsenic-based pesticides had been previously placed near a well that had supplied drinking water to the family from 1973 to 1989. The current arsenic level in the well water was 620 μg/L. No other high arsenic wells were found near the family’s residence. Based on these findings, it is possible to infer that the skin lesions exhibited by these family members were caused by long-term exposure to well water contaminated with arsenic-based pesticides. Additionally, biochemical analysis showed that the individuals exposed to arsenic had higher levels of aspartate aminotransferase and γ-glutamyl transpeptidase than those who were not exposed. These findings might indicate the presence of liver dysfunction in the arsenic-exposed individuals. This report elucidates the effects of arsenical compounds on the occurrence of high levels of arsenic in the environment and emphasizes the severe human health impact of arsenic exposure.
Implementing the ISO/IEC 17799 standard in practice - findings from small and medium sized software organisations

The ISO/IEC 17799 standard is commonly viewed as a necessary element in information security management. However, there is no empirical evidence of the usefulness of the standard in practice. This paper analyses the implementation experiences of four organisations that have implemented the ISO/IEC 17799 standard. Through semi-structured interviews, the results of the study suggest that the implementation of the standard has increased the understanding of information security in all personnel groups and the understanding of security has broadened from the technical aspects to corporate security. As downsides of implementing the ISO/IEC 17799 standard, the difficulties in deploying the standard, and the readability of the standard were criticised. The standard was also criticised because it does not directly affect the quality of the end product or service; it only has an indirect effect owing to the improved information security practices.
Fast Variational Analysis of On-Chip Power Grids by Stochastic Extended Krylov Subspace Method

This paper proposes a novel stochastic method for analyzing the voltage drop variations of on-chip power grid networks, considering lognormal leakage current variations. The new method, called StoEKS, applies Hermite polynomial chaos to represent the random variables in both power grid networks and input leakage currents. However, different from the existing orthogonal polynomial-based stochastic simulation method, extended Krylov subspace (EKS) method is employed to compute variational responses from the augmented matrices consisting of the coefficients of Hermite polynomials. Our contribution lies in the acceleration of the spectral stochastic method using the EKS method to fast solve the variational circuit equations for the first time. By using the reduction technique, the new method partially mitigates increased circuit-size problem associated with the augmented matrices from the Galerkin-based spectral stochastic method. Experimental results show that the proposed method is about two-order magnitude faster than the existing Hermite PC-based simulation method and many order of magnitudes faster than Monte Carlo methods with marginal errors. StoEKS is scalable for analyzing much larger circuits than the existing Hermit PC-based methods.
Chaotic complex spreading sequences for asynchronous DS-CDMA. I. System modeling and results

This paper and its companion are devoted to the evaluation of the impact of chaos-based techniques on communications systems with asynchronous code division multiple access. Sequences obtained by repeating a truncated and quantized chaotic time series are compared with classical m-sequences and Gold sequences by means of a performance index taken from communication theory which is here defined and thoroughly discussed. This analysis reveals that, unlike conventional sequences, chaotic spreading codes can be generated for any number of users and allocated bandwidth. Numerical simulations are reported, showing that systems based on chaotic spreading sequences perform generally better than the conventional ones. Some analytical tools easing the comprehension of these advantages are summarized.
TBPR: Trinity Preference based Bayesian Personalized Ranking for Multivariate Implicit Feedback

In e-commerce systems, user preference can be inferred from multivariate implicit feedback (i.e., actions). However, most methods merely focus on homogeneous implicit feedback (i.e., purchase). In this paper, we adopt another two typical actions, i.e., view and like, as auxiliaries to enhance purchase recommendation, whereby a trinity Bayesian personalized ranking (TBPR) method is proposed. Specifically, we introduce trinity preference to investigate the difference of users' preference among three types of items: 1) items with purchase action; 2) items with only auxiliary actions; 3) items without any action. Empirical study on the real-world dataset demonstrates that our method significantly outperforms state-of-the-art algorithms.
Fast reproducing web application errors

JavaScript has become the most popular language for client-side web applications. Due to JavaScript's highly-dynamic features and event-driven design, it is not easy to debug web application errors. Record-replay techniques are widely used to reproduce errors in web applications. However, the key events related to an error are hidden in the massive event trace collected during a long running. As a result, error diagnosis with the long event trace is exhausting and time-consuming. We present a tool JSTrace that can effectively cut down the web application error reproducing time and facilitate the diagnosis. Based on the dynamic dependencies of JavaScript and DOM instructions, we develop a novel dynamic slicing technique that can remove events irrelevant to the error reproducing. In this process, many events and related instructions are removed without losing the reproducing accuracy. Our evaluation shows that the reduced event trace can faithfully reproduce errors with an average reduction rate of 96%.
Mapping Heterogeneity Does Not Affect Wireless Coded MapReduce

The work considers a Coded MapReduce setting where computing nodes of different processing capabilities coexist. Motivated by scenarios where the mapping phase is performed by nodes of heterogeneous computing capabilities, we explore the setting with K1 nodes that can each map a fraction ${\gamma _1} \in \left[ {\frac{1}{K},1} \right]$ of the dataset, and K2 nodes that can each map a smaller fraction γ2 < γ1. For the standard wireless (single-antenna) device-to-device channel or its equivalent wired network with network-coding capabilities at the nodes, we propose a solution of assigning data to the nodes and a method of communicating intermediate values during the shuffling phase, that can be applied to any MapReduce problem and which entirely removes the affects of heterogeneity. The surprising outcome of this work is that the shuffling-phase delay is reduced by a factor of K1γ1 + K2γ2, matching the performance of the corresponding homogeneous setting, thus revealing for the first time that heterogeneity during the mapping phase does not inherently deteriorate the overall performance.
A mapreduce fuzzy techniques of big data classification

Due to the huge increase in the size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition a need for a means to meet the computational requirements to process such huge volume of data. The objective of this research is to implement a map reduce paradigm using fuzzy and crisp techniques, and to provide a comparative study between the results of the proposed systems and the methods reviewed in the literature. In this paper four proposed system is implemented using the map reduce paradigm to process on big data. First, in the mapper there are two techniques used; the fuzzy k-nearest neighbor method as a fuzzy technique and the support vector machine as non-fuzzy technique. Second, in the reducer there are three techniques used; the mode, the fuzzy soft labels and Gaussian fuzzy membership function. The first proposed system is using the fuzzy KNN in the mapper and the mode in the reducer, the second proposed system is using the SVM in the mapper and the mode in the reducer, the third proposed system is using the SVM in the mapper and the soft labels in the reducer, and the fourth proposed system is using the SVM in the mapper and fuzzy Gaussian membership function in the reducer. Results on different data sets show that the fuzzy proposed methods outperform a better performance than the crisp proposed method and the method reviewed in the literature.
RICOSRE: A Learning Model to Develop Critical Thinking Skills for Students with Different Academic Abilities

The purpose of this study was to investigate the effect of RICOSRE learning models towards critical thinking skills of students with different academic abilities. This quasi-experimental study employed a pretest-posttest nonequivalent control group design and involved all the eleventh-grade students from high schools in Malang, Indonesia. The sample consisted of 134 students from two separate schools which represented different academic abilities (high and low). Data were collected using an essay test on students’ critical thinking skills. A rubric was employed to assess students’ answers and the results were analyzed using ANCOVA. Findings suggest that (1) there is a difference between the critical thinking skills of students whose learning was facilitated with RICOSRE and critical thinking skills of students who were engaged in conventional learning, (2) there is a critical thinking difference between the high and low ability students, (3) there is a difference in students’ critical thinking skills affected by the interaction between learning models and students’ academic abilities. Based on the results of this study, it can be concluded that RICOSRE can close the critical thinking skills gap between the high and low ability students.
Attention in Options ∗

Investors’ attention should affect the option market as well as the stock market. Using the search frequency in Google as a measure of retail investor attention, we extend the literature on investor attention and stock returns in two dimensions. First, we document a significant positive relation between investor attention and option trading activity in terms of trading volume and open interest. The relation is particularly strong for put options. Second, we find a significant impact of investor attention on the riskiness of stocks, measured by either the option implied volatility or the realized volatility. The impact on the realized volatility is more significant and persistent and is driven by the idiosyncratic component while the impact on the option implied volatility is weaker and short-lived and is driven by the systematic component. A trading strategy by purchasing the high attention delta-hedged straddles and selling the low attention delta-hedged straddles generates an annual return of 6.44%, which can’t be explained by the standard risk factors.
Post-Quantum Cryptocash ?

In this paper, we extend our work in FC 2018 to construct an anonymous and decentralized cryptocash system which is potentially secure against quantum computers. In order to achieve that, we modify the the old linkable ring signature based on ideal-lattices to reduce the key size and give a complete security proof to it. The size of a signature in our scheme is O( n log μ log(2n)−log τ ), where μ is the cardinality of the ring, n is the security parameter, and τ = log μ. The framework of our cryptocash system follows that of CryptoNote with some modifications. By adopting the short quantum-resistant linkable ring signature scheme, our system is anonymous and efficient. We also introduce how to generate the verifying and signing key pairs of the linkable ring signature temporarily. With these techniques, the privacy of users is protected, even though their transactions are recorded in the public ledger.
Bounded model checking for knowledge and real time

We present TECTLK, a logic to specify knowledge and real time in multi-agent systems. We show that the model checking problem is decidable, and we present an algorithm for TECTLK bounded model checking based on a discretisation method. We exemplify the use of the technique by means of the "Railroad Crossing System", a popular example in the multi-agent systems literature.
PROPERTIES OF Cu-Al2O3 COMPOSITES OBTAINED BY HIGH- ENERGY MILLING AND INTERNAL OXIDATION

The inert gas atomized prealloyed copper powders containing 3.wt. %Al was milled up to 20 h in the planetary ball mill to oxidize aluminium in situ with oxygen from air. The microhardness of compacts processed from the as-received Cu-3 wt. %Al powder was 104 HV, while in the case of composites microhardness increases up to 300 HV. The grain size of compacted Cu-3 wt. %Al powder after 5 and 20 h of milling were 55 and 30 nm, respectively. Increased microhardness and improved thermal stability of compacted composites are the result of very small grain size of compacts and alumina particles formed by internal oxidation during high-energy milling. The values of electrical conductivity of compacted Cu-3 wt. %Al powder (37 and 38 % IACS after 5 and 20 h of milling, respectively) imply that the electrical conductivity depends not only on the presence of alumina particles but also on density of composites.
An efficient algorithm for Chinese license plate location

This paper presents an efficient and robust method of Chinese license plate location. In this method, firstly, 2-D wavelet transform is implemented to a digital car image to detect the high frequency information which is the basis to the next approaches. Secondly, mathematic morphology is used to get connected regions which contain the candidate plate regions with appropriate size. In order to pick out the real plate region, a BP neural network is trained by the samples chosen from plate images under different illumination conditions. Then certain of pixels in candidate regions are identified by the network based of color elements, which help determining the real plate location. Experimental results demonstrate the efficiency of our algorithm. Keywordslicense plate-location; wavelet transform; mathematical morphology; neural network;