Miracle-dz/UniFAR-FTUs huggingface dataset:
Interprocedural definition-use chains of dynamic recursive data structures

This dissertation presents a flow-sensitive algorithm to compute interprocedural definition-use chains of dynamic recursive data structures. The goal is to relate the statements that construct links of dynamic recursive data structures (i.e. definitions) to the statements that might traverse the structures through the links (i.e. uses). Specifically, for each statement S that defines links of recursive data structures, the algorithm finds the set of statements that traverse the links which are defined by S. The algorithm solves the definition-use chaining problem by performing backward iterative data flow analysis to compute the set of upward exposed uses at each statement. Definition-use chains of dynamic recursive data structures can be used to improve existing pointer analysis algorithms, since they provide the connections between traversal patterns and construction operations of recursive data structures. Without the knowledge of traversal patterns, for each statement p.f = q that builds a link from p to q, compilers have to estimate possible shapes or aliases of all paths that travel from any pointers to q through edge p.f and then from q to all pointers. Traversal patterns can improve pointer analysis by narrowing down the number of possible paths, and definition-use chains of dynamic recursive data structures connect statement p.f = q with its traversal patterns. This dissertation will propose an approach, called traversal-pattern-sensitive pointer analysis, that performs pointer analysis with the information of traversal patterns. Traversal-pattern-sensitive pointer analysis can be applied to parallelization or optimizations on programs with complex recursive data structures. This dissertation will present a dependence analysis algorithm that can identify parallelism in programs even with cyclic recursive data structures. The algorithm first applies the traversal-pattern-sensitive shape analysis to identify the traversal patterns of programs with cyclic graphs, isolate the statements that traverse or define the links of traversal patterns, and estimate the possible shapes of both overall structures and traversal patterns. It then uses the result of traversal-pattern-sensitive shape analysis to analyze the access conflicts between the sets of read and write references. Consequently, this approach can identify parallelism based on data dependence and shape analysis on programs even with cyclic recursive data structures.
Speaker Profiling for Forensic Applications

Speech signals convey important paralinguistic information such as age, gender, body size, language, accent and emotional state of speakers. Automatic identification of speaker traits and states has a wide range of forensic, commercial and medical applications in real-world scenarios. This thesis proposes a novel approach for automatic estimation of four forensically important components of speaker profiling systems, namely speaker age, height, weight and smoking habit estimations, from spontaneous telephone speech signals. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a constrained factor analysis on GMM weight supervectors. Then, Artificial Neural Networks (ANNs) and Least Squares Support Vector Regression (LSSVR) are employed to estimate age, height and weight of speakers from given utterances. Various classification techniques such as ANNs, Logistic Regression (LR), Naive Bayesian Classifier (NBC), Gaussian Scoring (GS) and Von-Mises-Fisher Scoring (VMF) are also utilized to perform smoking habit detection. Since GMM weights provide complementary information to GMM means, a score-level fusion of the i-vector-based and the NFA-based recognizers is considered for speaker age estimation and smoking habit detection tasks to improve the performance. In addition, inspired from the human learning system in which the related tasks are learned in interaction with each other, a multitask speaker profiling approach is proposed to evaluate the correlated tasks simultaneously, and consequently, to boost the accuracy in speaker age, height, weight and smoking habit estimations. To this end, a hybrid architecture involving the score-level fusion of the i-vector-based and the NFA-based recognizers is proposed. ANNs are then employed to provide an appropriate architecture to share the learned information with all tasks while they are learned in parallel. The proposed method has two major distinctions with the previous speaker profiling approaches. First, information in both GMM means and weights is employed through a score-level fusion of the i-vector-based and the NFA-based recognizers. Second, by applying multitask learning, correlated tasks, which are usually investigated in isolation, are evaluated simultaneously and in interaction with each other. The suggested approach is evaluated on telephone speech signals of National Institute for Standards and Technology (NIST) 2008 and 2010 Speaker Recognition Evaluation (SRE) corpora. Experimental results over 1194 utterances show the effectiveness of the proposed method in automatic speaker profiling.
Object-based classification approach for greenhouse mapping using Landsat-8 imagery

Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities. With their enormous input of fertilizers and pesticides, greenhouses have considerably changed the local soil quality and environmental risk factors. The ability to obtain timely and accurate information regarding the spatial distribution of greenhouses could make an important contribution to local agricultural management and soil protection. This paper attempts to present a practical framework for extracting suburban greenhouses, integrating remote sensing data from Landsat-8 and object-oriented classification. Inheritance classification was implemented, and various properties, including texture and neighborhood features in addition to spectral information, were investigated through the popular random forest technique for feature selection prior to SVM classification to improve the mapping accuracy. The results demonstrated that object-based classification incorporating non-spectral features yielded a significant improvement compared with the classification results obtained using only the spectral information in traditional per-pixel classification. Both the producer’s and user’s accuracy were higher than 85% for greenhouse identification. Although it remained a challenge to completely distinguish greenhouses from sparse plants, the final greenhouse map indicated that the proposed object-based classification scheme, providing multiple feature selections and multi-scale analysis, yielded worthwhile information when applied to a continuous series of the freely available Landsat-8 imagery data. Keywords: greenhouse, mapping, Landsat-8, object-based classification, feature selection, multi-scale DOI: 10.3965/j.ijabe. 20160901.1414 Citation: Wu C F, Deng J S, Wang K, Ma L G, Tahmassebi A R S. Object-based classification approach for greenhouse mapping using Landsat-8 imagery. Int J Agric & Biol Eng, 2016; 9(1): 79－88.
Efficient QRD-M Detection in the MIMO-OFDM System

In this paper, the improved QR-decomposition with M algorithm (QRD-M) for efficient signal detection is proposed. In order to improve efficiency in terms of complexity and detection performance, the proposed detection scheme adjusts the number of candidates according to the layer. Simulation results show that the proposed scheme has an improved detection performance compared to the conventional scheme with similar complexity.
Challenges of Network Forensic Investigation in Virtual Networks

The evolution of virtualization techniques is changing operating principles in today’s datacenters. Virtualization of servers, networks and storage increases the flexibility and dynamic of the environment by reducing the administrative overhead. Based on a physical underlay network, different logical networks are implemented with new protocols like VXLAN, STT or GENEVE. New paradigms like Software-Defined-Networks or Network Function Virtualization offer new capabilities to redesign the whole network infrastructure. This trend creates new challenges for digital investigations analysing incidents by extracting and interpreting recorded data inside the environment. As a branch of digital investigation, network forensic investigation is used to examine network traffic by capturing the data of a suspicious target system and analysing this data. In this article, we analyse in detail new challenges in investigating virtual networks. We propose a classification in three categories, which might help to develop new methods and possible solutions to simplify further necessary investigations in virtual network environments. The defined challenges are classified according their potential to impede the investigation. Based on this classification we derive a list of basic conditions, describing different necessary requirements to implement a successful, valid and ongoing network forensic investigation in these virtual networks. Journal of Cyber Security, Vol. 5 2, 15–46. doi: 10.13052/jcsm2245-1439.522 c © 2017 River Publishers. All rights reserved. 16 D. Spiekermann and T. Eggendorfer
The use of cultural algorithms with evolutionary programming to control the data mining of large-scale spatio-temporal databases

We use an evolutionary computational approach based upon cultural algorithms to guide the incremental learning decision trees by ITI. The results are compared to those produced by ITI itself for a complex real-world database. The results suggest that ITI can indeed produce optimal trees in some cases, and can produce optimal trees using an evolutionary approach in others.
Concept of Triangle: Examples of Mathematical Abstraction in Two Different Contexts

Geometry has abstract notions to be learnt so that all those notions cannot be just transferred into students’ mind like a bunch of information that should be memorized. Students need to construct those concepts during their learning process. This process of knowledge construction can be considered as an abstraction process. This study aimed to qualitatively compare abstraction process of students who learned the topic of triangle in conventional method and in van Hiele model of teaching aided by Geometers’ sketchpad. Subjects of this study were junior high school students in grade 7. This is a qualitative study with grounded theory design. Data were collected through classroom observation, test, and task-based interview. Results of the study show that theoretical abstraction processes tend to dominate classrom with conventional method of teaching while classroom with van Hiele model of teaching aided by Geometers’ sketchpad accommodated empirical abstraction process of the students.
A 250-mW, 8-b, 52-Msamples/s parallel-pipelined A/D converter with reduced number of amplifiers

A parallel-pipelined A/D converter with an area and power efficient architecture is described. By sharing amplifiers along the pipeline and also completely eliminating the amplifier from the last stage, an 8-b pipeline is realized using just three amplifiers (instead of seven amplifiers with a conventional pipeline architecture). By using two such pipelines in parallel, a 52 Msamples/s prototype A/D converter that is Intended for a switched digital video application has been implemented in a 0.9-/spl mu/m CMOS technology. The device occupies 15 mm/sup 2/ and dissipates 250 mW from a 5 V supply.
Feedforward compensation techniques for high-frequency CMOS amplifiers

Two circuit techniques for broadbanding the gain bandwidth product (GBW) of CMOS amplifiers are presented. The conventional feedforward technique using an AC bypass capacitor is reviewed. Compared to the conventional technique, the new techniques offer the advantage of exact cancellation of the low-frequency pole-zero pair. Furthermore, the position of the pole-zero pair occurs at higher frequencies than with the conventional technique. Calculations and computer simulations suggest that using the new techniques. CMOS amplifiers with a unity stable GBW of 160 MHz can be obtained in a standard 3- mu m CMOS technology. >
Personalized Web Content Recommendation based on LDA Profile

We propose a web content recommendation method based on latent topic modeling such as LDA. The main technical challenge is how to symbolize web access actions, by words, which are monitored through a web proxy log. We have developed a hierarchical URL dictionary and a cross-hierarchical directory matching method which provides automatic abstraction functionality. We also propose a recommendation scheme based on LDA model which recommends unseen contents as well as seen ones in the past. We show recommendation effectiveness of our method by applying to proxy data of 7500 students in Osaka University.
A roadmap to the utilization of intelligent information agents: are intelligent agents the link between the database and artificial intelligence communities?

The main focus of this paper is on the use of intelligent agents to gather, filter, and fuse information in the database domain, and as it pertains to agent security, agent communication, and information search and retrieval methods. It is intended as a roadmap for answering the question "are intelligent agents the link between the database and the artificial intelligence communities? ".
SAE Light Controller - Automatic vehicle location using mobile devices

Localizacao Automatica de Veiculos (AVL) e um meio para determinar e transmitir a local- izacao geografica de um veiculo automaticamente. Hoje em dia, o Sistema de Posicionamento Global (GPS) e a implementacao mais estavel de sistemas de localizacao baseados em satelites, sendo, consequentemente, a forma mais comum de determinacao de localizacao de um veiculo em sistemas AVL.Atualmente, nas grandes cidades, os transportes publicos tem um sistema AVL muito preciso, mas muito caro, instalado em cada veiculo, tornando-o inviavel para os pequenos operadores de transportes publicos para adquirir este tipo de equipamento. Por outro lado, os dispositivos moveis com GPS estao a tornar-se cada vez mais presentes, enquanto o seu poder de processamento e as baterias estao a melhorar.Este trabalho pretende propor o uso de dispositivos moveis como uma alternativa aos sistemas tradicionais AVL para transportes publicos. Estes dispositivos representam uma opcao mais barata para os operadores de transportes publicos, o que proporciona as pequenas empresas um metodo mais eficiente, em termos de custo, de manter o controlo das posicoes dos seus veiculos. Alem disso, este trabalho tambem pretende propor um algoritmo para a identificacao, em tempo real, de desvios do cronograma planeado.O primeiro passo consiste em analisar algoritmos para geolocalizacao, a fim de identificar o mais preciso, dado um percurso predefinido do transporte publico. O segundo passo e a imple- mentacao de uma interface e funcionalidades de um AVL orientado a dispositivos moveis para o calculo da Previsao do Tempo de Viagem (TTP). O terceiro passo e o desenvolvimento de um algoritmo, usando filtros de Kalman, que preve desvios no cumprimento do horario em tempo real e informa os motoristas de autocarros e passageiros destes desvios. Finalmente, a ultima etapa e comparar os resultados obtidos no presente sistema com o ja em uso no mercado, no que respeita a relacao custo-eficiencia entre eles, a fim de determinar a viabilidade do sistema desenvolvido para o uso em transportes publicos.
Design and Implementation of the Cloud based Application for Text Mining Tasks

Main objective of the presented work was to design and implement the visualization solution for text mining tasks. Specific goal within the effort presented here was to design the decision tree classifier visualization focused mostly on specific aspects of text classification. Visualization tool is based on commonly used decision tree visualization techniques, which are modified to specific conditions of visualizations of large data. Designed solution was implemented in Processing language on top of Java based text mining library and tested on several standard textual document collections. Solution was integrated to the web portal providing text mining services to enhance the experience of solving text mining tasks by addition of a module providing a visual feedback of constructed models.
An approach for the online optimized control of a hybrid powertrain

The basic objective for the control of a hybrid electric vehicle is the powertrain's operation with minimum energy consumption. At a parallel hybrid, the fundamental problem is the optimal split of the torques of combustion engine and electric motor at an optimum engine speed. It has to be solved "online" at every point of time during the operation. At actual hybrid vehicles, this function is realized by an operation strategy, which is based on a static specification, developed on empirical considerations with "optimized" strategy parameters. This kind of control works fast and reliably, but often produces results far away from all optimal control. To meet higher requirements, the paper discusses direct use of optimization methods to solve this problem. In the past, fundamental research on the Autark Hybrid's powertrain control had been done by computing the optimum control vectors for given driving cycles by a special offline optimization method. The objective was the overall fuel consumption at the end of a cycle. For online optimization, however, it is necessary to specify an objective which relates to each point of time and which defines the problem of latching electrical energy. The paper presents a new approach with an objective based on the minimization of the powertrains amount of power dissipation.
Job Schedulers in Mapreduce

The aim of this paper is to provide a better understanding of job schedulers in MapReduce and identify important research directions in this area. it present advantages and disadvantages of different jobs scheduling algorithm in MapReduce also job schedulers in MapReduce and then their features and the application of each category of the schedulers are expressed and the Mapreduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Energy-Efficient Load-Aware VM Placement using Multi-Metrics Analysis

Objective: Cloud data centres comprise of a huge number of physical machines which host a number of heterogeneous virtual machines. These VMs exhibit fluctuating behaviour in terms of resource usage and specifications leading to disproportionate resource utilization in physical machines. This load imbalance on a host degrades its performance and violates many SLA features. To maintain even load in a data centre, an effective virtual machine placement technique is required. This paper proposes an efficient VM placement and migration technique based on three-tier architecture and considers load as a prime objective. Method: To maintain an even-load in a data centre, many load balancing techniques have been suggested which consider either migration cost or heterogeneity as the overall objective. This paper uses a multi-metrics analysis to distribute VMs evenly and maintains a stable equilibrium inside a data centre. We have applied Analytical Hierarchy Process (AHP) for efficient virtual machine placement using four post placement metrics which defines some of the key Service Level Agreement (SLA) parameters. The metrics considered for placement and migration are all load-centric and promise fewer migrations and SLA violations. Findings: Simulation results show a remarkable reduction in migrations which improves energy conservation inside the data centre. Application of AHP in balancing a data centre’s load is still unexplored. The presented placement technique selects the best candidate machine for placement, hence upgrades the performance. Improvement: Further enhancement includes adding more metrics for placement to fine tune performance and ensure better resource utilization along with reduction in overall energy consumption of a data centre.
Probabilistic Recursive Cryptanalysis of Ultralightweight Mutual Authentication Protocols for Passive RFID Systems

Security and privacy are the fundamental concerns of RFID systems. Several ultralightweight mutual authentication protocols have been proposed to ensure the security of RFID systems in cost effective manner. These protocols usually involve simple bitwise logical operations such as XOR, AND, OR and some special purpose ultralightweight primitives. In this paper, we identify the vulnerabilities of the two recently proposed ultralightweight mutual authentication protocols: SASI and Yeh. et al. We have used Recursive Linear Cryptanalysis (RLC) for security analysis of SASI protocol, which requires only two authentication sessions to reveal concealed secret ID of the tags. For Yeh et al. protocol, we have proposed an active Quasi-Linear attack, which requires approximately 213 authentication sessions to disclose the tag’s secret ID.
Learning Theory and Learning Objects

This paper applies some of the core principles of learning theory to the design and use of learning objects. It considers three prominent schools of thought: behaviourism, constructivism, and situated learning theory, draws out principles which are of relevance to learning objects, and describes an example which illustrates how these principles might apply in practice. Some particular themes which emerge are: • Sequencing and narrative: how to link learning objects together in a coherent learning programme. • Learning Objectives, outcomes and goals: their important role, and the differences between the schools in where they (and other metadata elements) might be located: “intrinsic” to the objects, or in the surrounding environment. • Context and authenticity of learning - including social context: how learning objects can provide and relate to this. • The learner’s engagement in the learning process . Each of the schools is felt to have valid principles which are applicable to learning objects, depending on the domain, and the orientation of the providers.
A complexity analysis of statistical learning algorithms

We apply information-based complexity analysis to support vector machine (SVM) algorithms, with the goal of a comprehensive continuous algorithmic analysis of such algorithms. This involves complexity measures in which some higher order operations (e.g., certain optimizations) are considered primitive for the purposes of measuring complexity. We consider classes of information operators and algorithms made up of scaled families, and investigate the utility of scaling the complexities to minimize error. We look at the division of statistical learning into information and algorithmic components, at the complexities of each, and at applications to support vector machine (SVM) and more general machine learning algorithms. We give applications to SVM algorithms graded into linear and higher order components, and give an example in biomedical informatics.