Miracle-dz/UniFAR-FTUs huggingface dataset:
Attacking an AI Classifier in a Realistic Context

In this thesis a theoretical attack on an AI classifier as introduced by Barreno et al. (2006) is described and lifted to a more realistic setting. The dataset, classifier and attack algorithm were updated in order to increase reality. The KDD ’99 cup-data on network intrusion was used as a data set and a combination of kMeans and Learning Vector Quantization was used for classification. The hypothesis was confirmed that an increase in realism would result in a significant increase in the number of iterations needed for a successful attack. However in some attampts the attack still succeeded. Subsequently the randomization defense as suggested by Barreno et al. was implemented and tested in both abstract and realistic contexts. The defense was effective in all tested contexts, however seemed to be less effective in the most realistic one. Since both the realistic context and the randomization defense increase the number of iterations needed for the attack, further research can be performed on how an external network notifying suspicious changes in the primary classifier may benefit from this.
Material point method and its applications

Material point method (MPM) discretizes the material domain by particles, and solves the momentum equations on a predefined background mesh. MPM avoids mesh entanglement and involving convection term, by taking advantages of both the Lagrangian and the Eulerian methods. So it is very promising in the numerical simulation of problems involving extreme material deformation and fracture. The state of the art of the theory, the algorithm development and implementation, and the software development of material point method are reviewed in detail. The generalized material point method, the contact algorithm, the adaptive algorithm, the parallel algorithm, and the coupling with other methods are included. Applications of MPM to the problems involving extreme material deformation, such as hypervelocity impact, penetration, explosion, dynamic fracture, fluid-structure interaction, multiscale analysis, and granular material flow and rock and soil failure, are investigated systematically. The applications demonstrate the advantages of MPM over conventional numerical methods.
On the complexity of multiprocessor task scheduling

The problem addressed here is the complexity of scheduling multiprocessor tasks, i.e. tasks which may require more than one processor simultaneously. This model seems natural for many parallel applications, however, is different than the standard approach in the scheduling theory. In this paper, we analyse both cases when the task requires a number of processors simultaneously, and when the task requires a set of processors simultaneously. In the former case the problem of preemptive scheduling is NP-hard in general. Next, we show that the problem of non-preemptive scheduling on four processors, when tasks can be executed on any subset of processors, is solvable in pseudo-polynomial time in the absence of uni-processor tasks. Finally, the problem of non-preemptive scheduling multiprocessor tasks, requiring a set of processors simultaneously for L max criterion, is shown to be strongly NP-hard even for two processors.
A Litterbag Study: Decomposition Rate and C/N Ratio of Annual Crop Biomass Residues on An Ultisols in Natar Village, South Lampung, Indonesia

To evaluate the decomposition rate and the C/N ratio of biomass residues from several crops that are the main food sources in Indonesia, a litterbag study was conducted from February to November 2016 on ultisols in Natar Village, South Lampung, Indonesia. There were four types of crops biomass residues used i.e., maize stovers, rice straws, shoots and leaves of cassava, and soybean stovers in the form of fresh or compost. A fifty gram dry weight of biomass was put into a litterbag, placed above the ground and at a depth of about 10-20 cm, and incubated for nine months. The remaining biomass (dry weight), the content of Organic-C, Total-N, and C/N levels of the remaining biomass in the litterbag were observed every month until nine months. The dry weight of biomass was obtained by an oven dried at 70°C for approximately 48 hours until reached the constant weight. The decomposition rates were measured as (k) = ln (X/Xo)/t. Results showed that the decomposition rate of the biomass residues of the four crops were different. The shoots and leaves of cassava biomass were most rapidly decomposed (k =-0.2830) and significantly different from others, followed by maize stover (k=-0.2066), rice straw (k = -0.1924), and soybean stover (k = -0.1675). Junita Barus, Jamalam Lumbanraja, Hamim Sudarsono and Dermiyati 388 Pertanika J. Trop. Agric. Sc. 42 (1): 387 - 403 (2019) Biomass of fresh or compost form and litterbag placement generally affected the decomposition rate of biomass residues. Keywords: Biomass, carbon, crop residues, decomposition, nitrogen
A Query Recommender System for Interactive Database Exploration

Recommender systems plays an important role in the information and e-commerce ecosystem. It represents a powerful method for enabling users to filter through large information and product spaces. The collaborative filtering systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. In this paper, a query recommender framework is instantiated to assist non-expert users of the scientific database by providing personalized query recommendations. For this, the fragment-based approach is proposed in which each user session is represented by the query fragments identified in the respective queries. In order to provide recommendation, the item-based collaborative filtering is used which is based on pair-wise similarity among the items involved in the recorded user sessions. The fragment-based approach provides better precision and recall than previous tuple-based approach.
Metallic monoclinic phase in VO2 induced by electrochemical gating: In situ Raman study

We report in situ Raman scattering studies of electrochemically top gated VO2 thin film to address metal-insulator transition (MIT) under gating. The room temperature monoclinic insulating phase goes to metallic state at a gate voltage of 2.6 V. However, the number of Raman modes do not change with electrolyte gating showing that the metallic phase is still monoclinic. The high-frequency Raman mode Ag(7) near 616 cm−1 ascribed to V-O vibration of bond length 2.06 Å in VO6 octahedra hardens with increasing gate voltage and the Bg(3) mode near 654 cm−1 softens. This shows that the distortion of the VO6 octahedra in the monoclinic phase decreases with gating. The time-dependent Raman data at fixed gate voltages of 1 V (for 50 minutes, showing enhancement of conductivity by a factor of 50) and 2 V (for 130 minutes, showing further increase in conductivity by a factor of 5) show similar changes in high-frequency Raman modes Ag(7) and Bg(3) as observed in gating. This slow change in conductance together with Raman frequency changes show that the governing mechanism for metalization is more likely due to the diffusion-controlled oxygen vacancy formation due to the applied electric field.
Perceptive particle swarm optimization algorithm for constrained optimization problems

A new Particle Swarm Optimization(PSO) algorithm was proposed to solve the constrained optimization problems.A feasibility-based rule handled constraints to update the individual optimal solutions and the global optimal solutions.Adaptive perceptive ability of particles can balance the global search and the local search.This ability can also help to avoid prematurity.The velocity of particle around the boundary was revised by the results of perceiving to enhance the search around the boundary.The simulation results show that the proposed approach has fast convergence and better optimization ability,and is suitable for solving constrained optimization problems.
Implementation of the iterative three-component heuristic for the team orienteering problem with time windows

This thesis studies the Team Orienteering Problem with Time Windows (TOPTW). In this problem, a set of locations is given and each of these locations has a service time, a proﬁt and a time window. The objective is to maximize the proﬁt by visiting the locations, while taking into account the time window of the locations. The aim of this thesis is to reproduce the iterative three-component heuristic (I3CH) by Hu and Lim (2014) for TOPTW. This heuristic consists of a local search and a simulated annealing which give solutions that can be improved by a third component: routing recombination. The heuristic is applied on test instances which is also used in Hu and Lim (2014). Results of the Hu and Lim (2014) are veriﬁed and we were also able so ﬁnd new best known solutions for the TOPTW. Furthermore, we adapted the I3CH for the Orienteering Problem with Hotel Selection and Time Windows. We tested the heuristic on a small set of benchmark instances which also give solutions near the best known solutions for these benchmark instances.
MECP: A Memory Efficient Real Time Commit Protocol

IntroductIon Important data base system resources are the data items that can be viewed as logical resource, and CPU, disks and the main memory which are physical resources [Garcia-Molina et al. 1992]. Though, the cost of main memory is dropping rapidly and its size is increasing, the size of database is also increasing very rapidly. In real time applications, where the databases are of limited size or are growing at a slower rate than memory capacities are growing, they can be kept in the main memory. However, there are many real time applications, which handle large amount of data and require support of an intensive transaction processing. The amount of data they store is too large (and too expensive) to be stored in the main memory. Examples include telephone switching, satellite image data, radar tracking, media servers, computer aided manufacturing etc. [Ramamritham, 1993]. In these cases, the database can not be accommodated in the main memory easily. Hence, many of these types of database systems are disk resident. The buffer space in the main memory is used to store the execution code, copies of files, data pages and any temporary objects produced. The buffer manager controls the main memory and the availability of main memory space affects transaction's response time [Garcia-Molina et al. 1992]. Before starting the execution of a transaction , buffer is allocated for the transaction. When the memory is running low, a transaction may be blocked from execution. The amount of memory available in the system thus limits the number of MECP concurrently executable transactions. In the large-scale real time database systems, the execution of the transaction will be significantly slowed down, if available main memory is low. When the total maximum memory requirement of the admitted transactions exceeds the available memory, distributed real time database systems (DRTDBS) must decide how much memory should be given to each transaction. This decision must also take into account the transactions' timing requirements to ensure that the transactions receive their required resources in time to meet their deadlines. In addition, the effectiveness of memory allocation in reducing individual transaction's response time should be considered, so as to make the best use of the available memory. Therefore, it is important for the database designer to develop memory efficient protocols, so that more number of transactions can be executed concurrently at any time instant. In this work, design of a distributed commit …
Synthesis and characterization of a novel hydroxypropyl chitosan-graft-β-Cyclodextrin copolymer as potential drug carrier

Abstract One of the most attractive fields in drug delivery is the design of nanoparticles that could be used to transport drugs into the target place at appropriate time. This paper describes the preparation of a novel hydroxypropyl chitosan-graft-β-cyclodextrin (HPCS-g-β-CD) copolymer using the 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) and N-hydroxysuccinimide (NHS) promoted condensation reaction. Structural characterization was finished with FTIR and 1H NMR spectra, which confirmed the grafting of β-cyclodextrin (β-CD) onto hydroxypropyl chitosan (HPCS). The thermal properties were studied based on DSC and TGA analyses, which revealed the fact that grafting of β-CD onto HPCS resulted in obvious enhancement of the decomposition temperature of the copolymer. Aqueous solution of HPCS-g-β-CD copolymer was stable at pH 7-8 based on the Zeta potential test. The morphological characteristics were investigated by SEM, TEM and AFM analysis, and the results indicated that this copolymer had three-dimensional structure which was suitable for loading drugs. Graphical Abstract
Energy-efficient data collection protocol in wireless sensor networks

Performances of present data collection protocols were not good.A data collection tree based data collection protocol was proposed.By reducing depth of nodes and number of internal nodes in the data collection tree,the protocol tried to reduce two main kinds of energy consuming that restrict system lifetime,energy consuming of node transferring data to the base station and node idle listening.Simulations show that the protocol prolonged system lifetime by about 36%.
WETLAND CHANGE ANALYSIS AND THEIR IMPACT ON DENSE VEGETATION BY SPATIAL APPROACH

Wetlands are the most important component of an ecosystem that provides ecological habitats and hence wetlands are supposed to be the indicators of healthy environment. It has been observed recently that the wetlands are affected by several anthropogenic impacts and climate change. Monitoring of the wetlands for their preservation has become mandatory. The present study uses MODIS satellite data of 2000 and 2014 for recording the temporal change in the status of the wetlands in Nagpur district, Maharashtra India. Least square regression correlation had been performed between wetlands area and corresponding (Normalized Difference Vegetation Index) NDVI of the selected wetlands. The study shows that the extent of the wetlands have reduced in the study area during 2000-2014. The Least square regression analysis result shows that vegetated areas within wetlands reduced during 2000 to 2014. This study highlights the present status of wetlands, which is helpful in future, conservation, assessment, and management of wetlands. The application of remote sensing and GIS techniques made analysis simpler and effective.
Effect of Indoleacetic Acid and Zinc Sulphate Application on Growth and Some Physiological Parameters of Cowpea (Vigna sinensis Savi) Plants

This study was conducted in Koya city, Iraq on cowpea (Vigna sinensis Savi) plants California black eye variety by factorial experiment including the effects of foliar spraying of Zinc Sulphate ZnSO4.7H2O (ZS) or the plant growth regulator Indoleacetic Acid C10H9NO2 (IAA). Analysis of data revealed that ZS and IAA application affects significantly on the studied traits. 100 ppm of IAA increased significantly the leaves content of each of chlorophylls a, b, and total, although it decreased the content of total carotenoids. The results showed that the leaf area significantly increased by increasing the amount of ZS to 2.0 g/L compare with other treatments except 1.0 g/L ZS application. Foliar application of IAA increased significantly the dry matter percent of shoots and roots as well as the number of nodules per plant. It has been found that leaves content of zinc is related to the concentration of ZS applied significantly compared to the control and IAA treatments, however, the application of IAA caused to decrease K and Zn contents in leaf of the plants compared to other treatments. Finally, based on the results the best ratio of IAA for increased stomata number on the abaxial and adaxial leaves surfaces was 75 ppm.
Interactive Freeform Editing Techniques for Large-Scale, Multiresolution Level Set Models

Level set methods provide a volumetric implicit surface representation with automatic smooth blending properties and no self-intersections. They can handle arbitrary topology changes easily, and the volumetric implicit representation does not require the surface to be re-adjusted after extreme deformations. Even though they have found some use in movie productions and some medical applications, level set models are not highly utilized in either special effects industry or medical science. Lack of interactive modeling tools makes working with level set models difficult for people in these application areas. This dissertation describes techniques and algorithms for interactive freeform editing of large-scale, multiresolution level set models. Algorithms are developed to map intuitive user interactions into level set speed functions producing specific, desired surface movements. Data structures for efficient representation of very high resolution volume datasets and associated algorithms for rapid access and processing of the information within the data structures are explained. A hierarchical, multiresolution representation of level set models that allows for rapid decomposition and reconstruction of the complete full-resolution model is created for an editing framework that allows level-of-detail editing. We have developed a framework that identifies surface details prior to editing and introduces them back afterwards. Combining these two features provides a detail-preserving level set editing capability that may be used for multi-resolution modeling and texture transfer. Given the complex data structures that are required to represent large-scale, multiresolution level set models and the compute-intensive numerical methods to evaluate them, optimization techniques and algorithms have been developed to evaluate and display the dynamic isosurface embedded in the volumetric data.
Culture Bound Bridging The Cultural Gap In Language Teaching

Thank you for reading culture bound bridging the cultural gap in language teaching. As you may know, people have search hundreds times for their chosen books like this culture bound bridging the cultural gap in language teaching, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful bugs inside their computer.
Stock Prediction Using Artificial Neural Networks

Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks ANN have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. In this paper ANN modeling of stock prices of selected stocks under BSE is attempted to predict closing prices. The network developed consists of an input layer, one hidden layer and an output layer and the inputs being opening price, high, low, closing price and volume. Mean Absolute Percentage Error, Mean Absolute Deviation and Root Mean Square Error are used as indicators of performance of the networks.
The Effect of Multimedia Computer Assisted Language Learning (MCALL) on Iranian EFL Learners’ L2 Listening Comprehension

The use of technology, especially CALL, in language teaching and learning has received momentum. Additionally, incorporation of multimedia into CALL (MCALL) has opened a new horizon in education in the last decade. To shed more light on the pedagogical implications of MCALL, this study focused on the effect of MCALL on the listening comprehension of intermediate EFL learners. To do so, 30 homogeneous male and female participants were selected from a poll of EFL learners and were randomly assigned into two groups: the experimental and comparison groups. The former received 10 sessions of instruction using the MCALL, while the latter was exposed to the mainstream of conventional method of teaching listening. T-test analysis of the post-test results revealed a significant difference between the groups as the former group outperformed over the latter one in developing listening comprehension. The findings revealed that the use of MCALL could improve the learners’ listening skill.
Elasticsearch: The Definitive Guide

Whether you need full-text search or real-time analytics of structured data-or both-the Elasticsearch distributed search engine is an ideal way to put your data to work. This practical guide not only shows you how to search, analyze, and explore data with Elasticsearch, but also helps you deal with the complexities of human language, geolocation, and relationships. If you're a newcomer to both search and distributed systems, you'll quickly learn how to integrate Elasticsearch into your application. More experienced users will pick up lots of advanced techniques. Throughout the book, you'll follow a problem-based approach to learn why, when, and how to use Elasticsearch features.
Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organizations storage or compute capacity for accurate and timely decision making . Big data is not a single technology but a combination of old and new technologies that helps companies gain actionable insight. Therefore, big data is the capability to manage a huge volume of disparate data, at the right speed, and within the right time frame to allow real-time analysis and reaction. As we note earlier in this chapter, big data is typically broken down by three characteristics: Volume:How much data Velocity:How fast that data is processed Variety:The various types of data Although its convenient to simplify big data into the three Vs, it can be misleading and overly simplistic. For example, you may be managing a relatively small amount of very disparate, complex data or you may be processing a huge volume of very simple data. That simple data may be all structured or all unstructured. Even more important is the fourth V:veracity. How accurate is that data in predicting business value? Do the results of a big data analysis actually make sense? Determining relevant data is key to delivering value from massive amounts of data. However, big data is defined less by volume which is a constantly moving target than by its ever-increasing variety, velocity, variability and complexity .
Natural Language Processing Tools

This chapter discusses a subset of Natural Language Processing (NLP) tools available for researchers and enthusiasts of computer science, computational linguistics, and other fields that may utilize or benefit from Natural Language Processing. Several tools are described in this chapter, along with background, algorithms used in brief, usages, and examples. While this chapter is not comprehensive, it provides an extensive exposure to various NLP tools through examples, and it aims at providing an overview of the resources available, and concentrates mainly on open-source applications. Open-source applications were chosen since they are freely available for download by all users. Commonly, open source software provides the code that makes up the tool, and allows for users to inspect the inner-workings of the tools, or even modify them. By using open source examples, readers of this chapter can extend their investigation of NLP tools beyond the pages of this text by investigating the tools outlined.