Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Preserving Data Mining Using Multiple Objective Optimization

Privacy preservation is that the most targeted issue in information publication, because the sensitive data shouldn't be leaked. For this sake, several privacy preservation data mining algorithms are proposed. In this work, feature selection using evolutionary algorithm and data masking coupled with slicing is treated as a multiple objective optimisation to preserve privacy. To start with, Genetic Algorithm (GA) is carried out over the datasets to perceive the sensitive attributes and prioritise the attributes for treatment as per their determined sensitive level. In the next phase, to distort the data, noise is added to the higher level sensitive value using Hybrid Data Transformation (HDT)method. In the following phase slicing algorithm groups the correlated attributes organized and by this means reduces the dimensionality by retaining the Advanced Clustering Algorithm (ACA). With the aim of getting the optimal dimensions of buckets, tuple segregating is accomplished by Metaheuristic Firefly Algorithm (MFA). The investigational consequences imply that the anticipated technique can reserve confidentiality and therefore the information utility is additionally high. Slicing algorithm allows the protection of association and usefulness in which effects in decreasing the information dimensionality and information loss. Performance analysis is created over OCC 7 and OCC 15 and our optimization method proves its effectiveness over two totally different datasets by showing 92.98% and 96.92% respectively.
Assessment of genetic components for different traits in maize (Zea mays L.).

A complete 6 x 6 diallel mating design was followed to assess the genetic components for different traits in maize (Zea mays L.). Parents and F1 generations were sown in RCBD with three replications in the Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, Pakistan during 2012. The data regarding plant height, leaf area, number of kernels per ear, stomatal size, stomatal frequency, leaf venation and 100-grain weight in maize were recorded at maturity to assess the genetic components and combining abilities. Analysis of variance was done to assess the significance of genetic differences among genotypes. Graphical approach used for diallel studies showed that most of the traits studied were under control of additive gene action. Plant height, leaf area, stomatal frequency and stomatal size were found under control of non-additive gene action. Whereas ear diameter, ear length, number of kernels per ear, number of kernels per row, leaf venation and 100-grain weight were under control of additive gene action.
Optimum Extraction Technology for the Seed Oil of Nigella sativa L.

Seed oil of Nigella sativa L. is a popular edible oil in Egypt. How to improve the extraction efficiency of the oil will expand the use of the resource. In this paper, the four extraction factors, particle size of the seed, liquid-seed ratio, extraction time, and temperature, were optimized by the single-factor and orthogonal experiment to increase the extraction yield and linoleic acid concentration. The results showed that the twice extraction technology could increase the oil rate of 23.55% compared with the once extraction technology. The extraction rate of the once extraction could reach 33.51% under the conditions of liquid-seed ratio: 9 : 1 mL/g, extraction temperature: 35°C, extraction time: 1 h, and particle size: 60 meshes. The optimum twice extraction conditions with the extraction rate of 43.78% were 8 : 1 ml/g, 40°C, 1.5 h, and 60 meshes. Besides, the highest concentration of linoleic acid (58.09 mg/g) was twice extraction condition with 7 : 1 mL/g, 40°C, 2 h, and 60 meshes.
Development of a cost-effective basal medium for in-vitro propagation of Anthurium ( Anthurium andreanum ) as an alternative for Murashige and Skoog ( MS ) medium

Generally, Murashige and Skoog (MS) medium is used for in-vitro micropropagation of Anthurium but it is very costly. Therefore the major objective of this study was to develop a cost-effective basal medium for micropropagation of Anthurium as an alternative to MS medium. In the present experiment, Albert's solution, which is a popular fertilizer mixture, developed for hydroponics systems was used. All experiments were arranged according to a completely randomized design with ten replicates. 2 kg of Albert's mixture in 25 L water was used as stock. Albert's solution (1 ml of stock in 1L of water) with four different pH values (i.e. 5.0, 5.2, 5.4 and 5.6) was compared with MS medium for culture establishment. Five different levels of Albert's solution (1, 2, 3, 4 and 5 ml/L) were tested for in-vitro embryo establishment. Germination percentage was recorded 10 days after inoculation. Then 10 different concentrations of Albert's solution (i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 milL) were compared for in-vitro multiplication and rooting. The hormones 1.5 mgl L BAP and 0.5 mgl L 2, 4-D were supplemented for multiplication while 1 mgl L BAP and 0.5 mglL IBA were added for rooting. Three months after inoculation, number of shoots, roots and leaves per shoot were recorded. The highest germination percentage (90%) was observed on Albert's medium with pH 5.4. The highest germination rate (85%) was recorded in 3 milL concentration of Albert's medium. After three months the highest shoot number (48.4) was recorded in MS media, which was not significantly different from 5 ml/L concentration of Albert's medium at p< 0.05. The significantly highest number of roots per plant (15.1) was observed in 7 milL of concentration level while it was about 8 in the control. The highest number of leaves per shoot (9.4) was observed on Albert's medium at a concentration of 5 ml/L. Leaves became yellowish color in 8, 9 and 10 mil L concentration of Albert's medium. Survival rate of acclimatized plantlets obtained from Albert's medium was about 90% compared to MS medium (65%). The total cost for 1L MS media was about Rs. 2251and the total cost of Albert's medium was about Rs. 1101-. It could be concluded that Albert's solution is a cost-effective alternative medium for MS for in-vitro propagation of Anthurium.
A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma

Purpose: Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is essential for surgical planning. This study aimed to develop and validate a prediction model for preoperative LN status evaluation in ICC patients. Methods and Materials: A group of 106 ICC patients, who were diagnosed between April 2011 and February 2016, was used for prediction model training. Image features were extracted from T1-weighted contrast-enhanced MR images. A support vector machine (SVM) model was built by using the most LN status-related features, which were selected using the maximum relevance minimum redundancy (mRMR) algorithm. The mRMR method ranked each feature according to its relevance to the LN status and redundancy with other features. An SVM score was calculated for each patient to reflect the LN metastasis (LNM) probability from the SVM model. Finally, a combination nomogram was constructed by incorporating the SVM score and clinical features. An independent group of 42 patients who were diagnosed from March 2016 to November 2017 was used to validate the prediction models. The model performances were evaluated on discrimination, calibration, and clinical utility. Results: The SVM model was constructed based on five selected image features. Significant differences were found between patients with LNM and non-LNM in SVM scores in both groups (the training group: 0.5466 (interquartile range (IQR), 0.4059-0.6985) vs. 0.3226 (IQR, 0.0527-0.4659), P<0.0001; the validation group: 0.5831 (IQR, 0.3641-0.8162) vs. 0.3101 (IQR, 0.1029-0.4661), P=0.0015). The combination nomogram based on the SVM score, the CA 19-9 level, and the MR-reported LNM factor showed better discrimination in separating patients with LNM and non-LNM, comparing to the SVM model alone (AUC: the training group: 0.842 vs. 0.788; the validation group: 0.870 vs. 0.787). Favorable clinical utility was observed using the decision curve analysis for the nomogram. Conclusion: The nomogram, incorporating the SVM score, CA 19-9 level and the MR-reported LNM factor, provided an individualized LN status evaluation and helped clinicians guide the surgical decisions.
Sensitivity of Dispersion Characteristics of a Sandwiched Coplanar Waveguide at 070 GHz to Changes of Device Dimensions

In this paper the simulated dispersion characteristics of a Coplanar Waveguide (CPW) sandwiched between two dielectric substrates with changing dimensions are presented and analyzed in order to find out the sensitivity of the CPW’s frequency dependent effective dielectric constant to various device dimensions’ changes. The analysis in this paper will help optimize the design and fabrication of the CPW.
Broadband flow noise prediction of an unducted low speed axial fan using a Zonal LES – FWH approach

Helmut Kühnelt, Alessandro Zanon, Michele De Gennaro, Daniel Langmayr, Domenico Caridi 1 Mobility Department, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria, E-Mail: {helmut.kuehnelt, alessandro.zanon}@ait.ac. at, michele.degennaro. phd@gmail.com 2 ANSYS Germany GmbH, 83624 Otterfing, Germany, E-Mail: daniel.langmayr@ansys.com 3 ANSYS Italia S.r. l. , 20122 Milano, Italy, E-Mail: domenico.caridi@ansys.com
A proposal for transformation of topic-maps into similarities of topics

Newer information ﬁltering and retrieval models like the Fuzzy Set Model or the Topic-based Vector Space Model consider term dependencies by means of numerical similarities between two terms. This leads to the question from what and how these numerical values can be deduced? This paper proposes an algorithm for the transformation of topic-maps into numerical similarities of paired topics. Further the relation of this work towards the above named information ﬁltering and retrieval models is discussed.
Controlling Procedural Modelling Interactively with Guiding Curves

Grammar-based procedural modelling on its own produces a larger space of generated models than is artistically desirable. Probabilistic sampling techniques can help search this result space for models that best ﬁt a set of constraints. We aim to provide a useful probabilistic search function that can be run at interactive rates to enable the short feedback loops artists require for incremental, exploratory design. We present a constraint for use in Sequential Monte Carlo optimiza- tion where artists draw curves to guide the generation of models. The high-level structure of models can be intuitively speciﬁed by our constraint framework, allowing for variation in low-level details to be automatically ﬁlled in. We present a real-time model editor to demonstrate the artistic utility of our method.
A Skip Connection Architecture for Localization of Image Manipulations

Detection and localization of image manipulations are becoming of increasing interest to researchers in recent years due to the significant rise of malicious contentchanging image tampering on the web. One of the major challenges for an image manipulation detection method is to discriminate between the tampered regions and other regions in an image. We observe that most of the manipulated images leave some traces near boundaries of manipulated regions including blurred edges. In order to exploit these traces in localizing the tampered regions, we propose an encoder-decoder based network where we fuse representations from early layers in the encoder (which are richer in low-level spatial cues, like edges) by skip pooling with representations of the last layer of the decoder and use for manipulation detection. In addition, we utilize resampling features extracted from patches of images by feeding them to LSTM cells to capture the transition between manipulated and non-manipulated blocks in the frequency domain and combine the output of the LSTM with our encoder. The overall framework is capable of detecting different types of image manipulations simultaneously including copy-move, removal, and splicing. Experimental results on two standard benchmark datasets (CASIA 1.0 and NIST’16) demonstrate that the proposed method can achieve a significantly better performance than the state-of-the-art methods and baselines.
The Design of the Multi-Scale Data Fusion Algorithm Based on Time Series Analysis

Time series is an indicator at different times on different values, arranged in chronological sequence. The basic idea of the multi-scale analysis by orthogonal transformation, and it is such as wavelet transform signal decomposition analysis on different scales. The timing analysis method is achieved through the model method. The process parameters of the dynamic data time-domain analysis method is a parametric model to fit the observed data, and then use this model to analyze the observational data and produce data system. The paper presents the design of the multi-scale data fusion algorithm based on time series analysis. Finally, the advantages of the new algorithm are elaborated from the estimation accuracy and simulation demonstrated the effectiveness of the new algorithm.
RASPBERRY PI BASED SOLAR CAMERA FOR CITY SURVEILLANCE

Due to ever increasing threats to security, surveillance has become very important these days. In this paper we have proposed a design for Raspberry Pi based security camera with an integrated PIR (passive infrared) module that is capable of working on a solar powered battery. The surveillance camera performs two main functions. First it captures different images based on fixed interval rate for a fixed interval duration. Second, the camera is also synchronised to take a picture everytime the PIR detects motion. The captured image are uploaded on web based gallery that will store the pictures according to the date and time.This web Gallery is based on PHP for the front end design and on LAMP server for the backend framework. The implementation using Lamp server provides access to the different workstations i.e Hospitals, Police stations etc. Every workstation can access server by using the secure login id and password which provides a secure access to it. As an additional functionality we have programmed the module in a way that it will also be able to send SMS alerts to the registered users in case some activity is detected by the camera and the PIR sensor.
Implication of PVPI and EVLWi in prognostic evaluation of acute paraquat poisoning

To analyze the correlation between the hemodynamic parameters pulmonary vascular permeability index (PVPI) and extravascular lung water index (EVLWi), and serum paraquat concentration in poisoned patients. Additionally, we set out to investigate their indications in prognostic evaluation of acute paraquat poisoning. A total of 45 paraquat poisoning patients from March 2014 to December 2016 were recruited. All patients received gastric lavage plus strengthened blood purification therapy. Non-invasive hemodynamic monitoring was performed to measure PVPI and EVLWi. Serum paraquat concentration was examined to calculate clearance rate by purification. Hemodynamic indexes were compared between survival and death groups to analyze their prognostic indications. Among all 45 patients who orally ingested 44.2 ± 10.3 ml paraquat, 18 of them (40.0%) were deceased, and showed significantly higher serum paraquat, poisoning time, volume plus higher PVPI and EVLWi than those in the survival group (p < 0.05). After paraquat poisoning, PVPI and EVLWi showed dynamic changes. The 2-5 d survival group showed lower PVPI or EVLWi than death group (p < 0.05). In the survival group, serum paraquat showed higher decreasing velocity than in the death group (p < 0.05). ROC analysis found higher value of EVLWi combined with PVPI in differential diagnosis of the end-point (AUC = 0.936). After blood purification, serum paraquat, PVPI, and EVLWi all significantly decreased. The overall curative rate of 45 patients receiving blood purification was 60.00%. PVIP and EVLWi have certain implications in prognostic evaluation of acute paraquat poisoning patients. Strengthened blood purification can decrease serum paraquat concentration and improve curative rate.
Geo-Location Estimation with Convolutional Neural Networks

We explore the extraction of geo-spatial information from purely visual data. Though there have been previous attempts to apply machine learning and computational algorithms to this fundamentally subtle task, to the best of our knowledge, there are no initiatives that have directly utilized Convolutional Neural Networks (CNNs) for the challenge of geo-localization. This project thus employs a methodology that explores CNNs in conjunction with an investigation of existing computer vision techniques to analyze images according to their geospatial attributes. We apply CNNs to coarse and fine grained image classification on Google Street View data to predict city and location within a city, respectively. We also apply our models to location-tag prediction on a diverse dataset of images from Flickr. Our results demonstrate that our trained CNNs excel at both coarse and fine classification tasks and approach near human accuracy levels for location tag prediction when applied to noisy, real-world, datasets.
Erhao Xie Identification of Biased Beliefs in Games of Incomplete Information Using Experimental Data

This paper studies the identification of players’preferences and beliefs in empirical applications of discrete choice games using experimental data. The experiment comprises a set of games with similar features (e.g., two-player coordination games) where each game has different values for the players’monetary payoffs. Each game can be interpreted as an experimental treatment group. The researcher assigns randomly subjects to play these games and observes the outcome of the game as described by the vector of players’actions. Data from this experiment can be described in terms of the empirical distribution of players’actions conditional on the treatment group. The researcher is interested in the nonparametric identification of players’preferences (utility function of money) and players’beliefs about the expected behavior of other players, without imposing restrictions such as unbiased or rational beliefs or a particular functional form for the utility of money. We show that the hypothesis of unbiased/rational beliefs is testable and propose a test of this null hypothesis. We apply our method to two sets of experiments conducted by Goeree and Holt (2001) and Heinemann, Nagel and Ockenfels (2009). Our empirical results suggest that in the matching pennies game, a player is able to correctly predict other player’s behavior. In the public good coordination game, our test can reject the null hypothesis of unbiased beliefs when the payoff of the non-cooperative action is relatively low.
Contributions of professional associations in Romania to prevent poverty and social exclusion by developing social businesses

Article Type: Full Length Research Article Global poverty is neither created by the poor, nor is it the result of any incapacity associated to the poor; instead, it is the outcome of the system built by all the people, the institutions they set up and the concepts they have formulated. It cannot be concluded that professional associations have had to become philanthropic associations through the establishment and development of specific social economy foundations that concentrate their activity on poverty issues. However, such associations have actually come to witness the above problems, namely the difficult situation of former employees from different sectors; an example in this respect could be the massive and the most successful privatization of all time oil companies, in 2004, when, by various subtle (compensatory) means, the new capitalists have fired more than 40,000 people, relegating them and their families to a life of poverty. Private oil companies have outsourced more services, which subsequently became the property of the capitalists managing them, at the expense of society; proving their clear interest in gaining significant profit. Therefore, professional associations in this area might become, among other things, active organizations within society, assuming a social role, for both their members from the oil industry and for non-members, for society at large. Professional associations promote the idea of a new form of capitalism and a new type of company (understood as a business), based on the generosity of people and defined by experts as "social business". The fundamental purpose is to eliminate the social, economic and environmental problems; reduce the consequences of many other problems in society, such as hunger, homelessness, disease, pollution, lack of education, which finally lead to national insecurity. ©2017 BluePen Journals Ltd. All rights reserved
Inertial oscillations and frontogenesis driven by a quadratic horizontal density variation

We consider inviscid rotating flow driven by a horizontally quadratic density variation in a horizontally unbounded slab. This configuration permits a similarity solution, removing the dependence on the horizontal coordinate from the vorticity and temperature equations, which are then solved by numerical integration along characteristics. At large values of Rossby number, the flow proceeds to a singularity in a similar manner to the non-rotating flow with the same initial conditions. At small values of Rossby number there are inertial oscillations of growing amplitude, which have been analysed using the method of multiple scales. The oscillations become desynchronised between the upper and lower parts of the domain, and static instability appears for a small fraction of each oscillation period. Eventually the oscillations give way to the rapid formation of a singularity, in contrast to geostrophic adjustment theory which predicts that a singularity will form only if the Rossby number is sufficiently large.
Adversarial Imitation Learning from State-only Demonstrations ∗ Extended Abstract

Imitation from observation (IfO) is the problem of learning directly from state-only demonstrationswithout having access to the demonstrator’s actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from a large set of previously inapplicable resources such as internet videos. In this paper, we propose a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). We demonstrate that our approach performs comparably to classical imitation learning approaches (which have access to the demonstrator’s actions) and significantly outperforms existing imitation from observation methods in high-dimensional simulation environments.