Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Routing and Power Allocation in FDMA Wireless Networks based on H∞ Fuzzy Control Strategy

Simultaneous routing and resource allocation has been considered in wireless networks for its performance improvement. In this paper we propose a cross-layer optimization framework for worst-case queue length minimization in some type of FDMA based wireless networks, in which the the data routing and the power allocation problem are jointly optimized with Fuzzy distributed H∞ control strategy . with the presented formulation based on the minimization of the queuing length in each node, the routing and resource allocation problem is formulated as a decentralized fuzzy H∞optimal control problem for a wireless mesh network. the presented control strategy determines the transmit power in FDMA systems in which each node has fixed set of powers to be allocated to its outgoing links. Using the proposed control strategy a robust routing performance will be achieved in the presence of unknown network delays in network modeling. also with using fuzzy decision rules in the proposed H∞ controller strategy, we try to improve the network performance criteria and avoid packet loss in the network.
Rationalizing Efficient Compositional Image Alignment The Constant Jacobian Gauss-Newton optimization algorithm

We study the issue of computational efficiency for Gauss-Newton (GN) non-linear least-squares optimization in the context of image alignment. We introduce the Constant Jacobian Gauss-Newton (CJGN) optimization, a GN scheme with constant Jacobian and Hessian matrices, and the equivalence and independence conditions as the necessary requirements that any function of residuals must satisfy to be optimized with this efficient approach. We prove that the Inverse Compositional (IC) image alignment algorithm is an instance of a CJGN scheme and formally derive the compositional and extended brightness constancy assumptions as the necessary requirements that must be satisfied by any image alignment problem so it can be solved with an efficient compositional scheme. Moreover, in contradiction with previous results, we also prove that the forward and inverse compositional algorithms are not equivalent. They are equivalent, however, when the extended brightness constancy assumption is satisfied. To analyze the impact of the satisfaction of these requirements we introduce a new image alignment evaluation framework and the concepts of shortand wide-baseline Jacobian. In wide-baseline Jacobian problems the optimization will diverge if the requirements are not satisfied. However, with a good initialization, a short-baseline Jacobian problem may converge even if the requirements are not satisfied.
A Framework to Develop Security Policies for Cloud Computing

A BSTRACT Cloud Computing has become a buzzword of today. Security is the major challenge for cloud computing. The majority of companies deploying cloud computing solutions have no cloud-specific security policies and procedures in place and have no measures to approve or evaluate cloud applications that use sensitive of confidential information. There is no integrated model-based framework is available to develop cloud security policies that deal with both internal and external security challenges. This paper proposes a framework to provide solution to the above problem. This frame work is useful in specifying Cloud security requirements and to develop policies to meet them .
MULTILAYER PERCEPTRON-MULTIACTIVATION FUNCTION ARTIFICIAL NEURAL NETWORK MODEL FOR MUNICIPAL WATER DEMAND FORECASTING

In this research, a multilayer perceptron neural network model with multiactivation function called (MLP-MAF) model has been developed for municipal water demand forecasting. The developed model uses different activation functions in the hidden layer neurons. Different combinations of the linear, logistic, tangent hyperbolic, exponential, sine and cosine activation functions were used in the hidden layer neurons. In order to assess the credibility of developed model results, the model was run over the available data which include the time series of daily and monthly municipal water consumption for fourteen years (1/1/1992 31/12/2004) of Tampa city, USA. Each time series was divided into two subsets: the estimate subset for fitting the model and the holdout subset for evaluating the forecasting ability of the model. Additionally, three statistical measurements, namely the coefficient of determination (R), the root mean square error (RMSE) and the mean absolute percent error (MAPE) and two hypothesis tests, namely the t-test and F-test have been reported for examining the forecasting accuracy of the developed model. The results show that the combination of linear, sine and cosine functions is better than other combinations. Furthermore, the effectiveness assessment of this model shows that this approach is considerably more accurate and performs better than the traditional multilayer perceptron (MLP) and radial basis function (RBF) neural networks.
Dynamic object offloading in Web services

In several application contexts, Web Services adoption is limited due to performance issues. Design methods often propose the adoption of coarse-grained interfaces to reduce the number of interactions between clients and servers. This is an important design concern since marshaling and transferring small parts of complex business objects might entail sensible delays, especially in high latency networks. Nevertheless, transferring large data in coarse-grained interactions might bring useless data on the client side, whereas a small part of the transferred object is used. To reduce data transfers, in addition to well-known techniques based on XML compression, a possible approach is considering a finer granularity at data level: results produced by Web services invocations could be transferred to the client by using incremental loading. However, existing Web service technology does not provide run-time infrastructures with an adequate support for lazy serialization. In addition, pure lazy serialization could incur in high overheads due to many interactions, especially in wide area networks. This paper presents a novel approach to extend existing Web services run-time supports with dynamic offloading capabilities based on an adaptive strategy that allows servers to learn clients behaviors at runtime. By exploiting this approach, service based applications can improve their performances, as experimental results show, without any invasive change to existing Web services and clients.
A Paral L El Program for the Recogni Ti on of P-invariant Segments

LetP beanarbitrary,butxedpermutationon0:: N), withN0. Inthispaperwedesi gn a paral l el program that determines for each segment of N successive elements of its input sequence whether it is invariant under P. The program consists of a linear arrangement of cel l s and contains|in addition to links between neighbour cells|connections between cel l s thatare arbitrarilyfar apart. For some well-known instances of the general problem, such as palindrome recognition and square recognition, the solutions are systolic and comparabl e to the \traditional" ones. Depending on P , however, the solutions may be non-systol i c. Figure 1 gives an impression of the obtained parallel palindrome recognizer. a a a a a b b b c c c N N-1 N/2-N+3-N+6 N/2-1 a a Figure 1: General program for palindrome recognition.
Asymptotic Analysis of Multiple Description Quantizers

A high-rate analysis of multiple description quantizers is presented for rth-power distortions and general source densities. Both, fixed-length and variable-length encoding of the quantizer indices are considered. Optimal companding functions are shown to be the same as for single-channel quantizers. As compared to the bound of Ozarow (1980), a gap of 8.69 dB and 3.07 dB exists between the entropy-constrained and level-constrained cases, respectively, for a memoryless Gaussian source and r=2.
Research on the missing attribute value data-oriented for decision tree

In the existing multiple choice methods of decision tree' test attributes, can't see such report as” Let missing data processing integrated in the selection process of test attributes”, however, the existing process methods of missing attribute value data can draw into bias in different degrees, base on this, propose an information gain rate base on combination entropy as the decision tree's testing attributes selection criteria, which can eliminate missing value arrtibutes' infulence on testing attributes selection, and be implemented on WEKA. The computational complexity of the MultiInfo Tree is better than that of C4.5.
Phishing Detection by Determining Reliability Factor Using Rough Set Theory

Phishing is a common online weapon, used against users, by Phishers for acquiring a confidential information through deception. Since the inception of internet, nearly everything, ranging from money transaction to sharing information, is done online in most parts of the world. This has also given rise to malicious activities such as Phishing. Detecting Phishing is an intricate process due to complexity, ambiguity and copious amount of possibilities of factors responsible for phishing. Rough sets can be a powerful tool, when working on such kind of Applications containing vague or imprecise data. This paper proposes an approach towards Phishing Detection Using Rough Set Theory. The Thirteen basic factors, directly responsible towards Phishing, are grouped into four Strata. Reliability Factor is determined on the basis of the outcome of these strata, using Rough Set Theory. Reliability Factor determines the possibility of a suspected site to be Valid or Fake. Using Rough set Theory most and the least influential factors towards Phishing are also determined.
Improving Video Streaming over IEEE 802.11 Mesh Networks through a Cross-Layer Design Technique

There has been a spate of interest in wireless mesh networks in recent years. The reason behind such an upsurge of interest is that wireless mesh networks are seen as enablers of ubiquitous broadband access applications such as the Internet. Moreover, wireless mesh networks provides benefits such as ease of installation, cost effective deployments, a high level of scalability in coverage area and capacity, network flexibility and self-configuration capabilities. These benefits enable the proliferation of seamless communication in underdeveloped areas such as rural communities. However, despite all these benefits, many research challenges still remain in wireless mesh networks. One such challenge is the support of real time applications such as video streaming. This paper presents a cross-layer optimization technique for real time applications provisioned over IEEE 802.11 wireless multihop networks. In this technique, packet error control and throughput optimization is established through rate adaptation mechanisms. Congestion control and optimal route selection in the network layer is achieved through routing protocol optimizations. Simulation results show that the proposed cross-layer optimization method significantly improves the performance of multihop wireless mesh networks when UDP is used as the transport protocol.
Tracing QTLs for Leaf Blast Resistance and Agronomic Performance of Finger Millet (Eleusine coracana (L.) Gaertn.) Genotypes through Association Mapping and in silico Comparative Genomics Analyses

Finger millet is one of the small millets with high nutritive value. This crop is vulnerable to blast disease caused by Pyricularia grisea, which occurs annually during rainy and winter seasons. Leaf blast occurs at early crop stage and is highly damaging. Mapping of resistance genes and other quantitative trait loci (QTLs) for agronomic performance can be of great use for improving finger millet genotypes. Evaluation of one hundred and twenty-eight finger millet genotypes in natural field conditions revealed that leaf blast caused severe setback on agronomic performance for susceptible genotypes, most significant traits being plant height and root length. Plant height was reduced under disease severity while root length was increased. Among the genotypes, IE4795 showed superior response in terms of both disease resistance and better agronomic performance. A total of seven unambiguous QTLs were found to be associated with various agronomic traits including leaf blast resistance by association mapping analysis. The markers, UGEP101 and UGEP95, were strongly associated with blast resistance. UGEP98 was associated with tiller number and UGEP9 was associated with root length and seed yield. Cross species validation of markers revealed that 12 candidate genes were associated with 8 QTLs in the genomes of grass species such as rice, foxtail millet, maize, Brachypodium stacei, B. distachyon, Panicum hallii and switchgrass. Several candidate genes were found proximal to orthologous sequences of the identified QTLs such as 1,4-β-glucanase for leaf blast resistance, cytokinin dehydrogenase (CKX) for tiller production, calmodulin (CaM) binding protein for seed yield and pectin methylesterase inhibitor (PMEI) for root growth and development. Most of these QTLs and their putatively associated candidate genes are reported for first time in finger millet. On validation, these novel QTLs may be utilized in future for marker assisted breeding for the development of fungal resistant and high yielding varieties of finger millet.
Security from various Intrusion Attacks using Honeypots in Cloud

Cloud Computing means accessing the data from their own datacenters such that the chances of eavesdropping have been reduced and storage cost is reduced. Now here in this paper an efficient technique of intrusion detection is proposed using the concept of honeypots. The data to be send by the cloudlets is accessed by the brokers of the cloud where we have implemented some of the rules for the detection of intrusions. The proposed technique implemented here provides less false alarm ratio and provides more accuracy for the detection of intrusions. I. INTRODUCTION
High-level controller for an arm rehabilitation robot - positioning algorithms with respect to EMG data

This paper touches upon the issue of dexterous control of rehabilitation robots. There have been proposed a high-level control system and algorithms used for training patients. Emphasis was made especially on the gravitation compensation algorithm, because of the low velocity and acceleration of movement parameters during rehabilitation. The direction of movements was estimated based on the model of surface electromyographic (sEMG) signal, which renders muscle activity very well. The model of manipulator is stable, behaves in a predictable way and, according to the EMG interpretation block, satisfyingly deduces the intention of motion and controls the model.
Evolving WCDMA for Improved High Speed Mobile Internet

WCDMA release five introduces peak bit rates for data services exceeding 8 Mbit/s together with lowered round-trip times. The system throughput is also increased. This is enabled through fast link adaptation, fast hybrid ARQ and fast scheduling. This paper describes the release five concept and illustrates its performance in single as well as multiple service scenarios.
A multicast transmission schedule for scalable multirate distribution of bulk data using nonscalable erasure-correcting codes

This paper addresses the efficient multicast dissemination of bulk data from a single server to numerous clients. The challenge is complex: a client may commence reception at arbitrary times, should receive as little "extra" data as possible until it can reconstruct the entire content, and should have flexibility in choosing the data rate. From the network perspective, the data rate over any link should be as close as possible to the maximum single-downstream-client subscription rate. Also, the solution should scale to huge files and numerous subscribers, and should withstand changing network conditions and packet loss. Finally, it should be friendly to other traffic. For any base client-subscription rate and integer factors thereof, we jointly achieve all these goals in a near-optimal way while using standard ("any k of N") block erasure-correcting codes. Scalability in file size is attained by breaking the file into equisized groups of equisized blocks and separately encoding each group. The other properties are attained by a unique open-loop layered multicast transmission schedule. Each client merely subscribes to one or more standard multicast groups. The need to use special, nonstandard and possibly proprietary codes that scale well is thus obviated.
A Wide Dynamic Range CMOS Image Sensor With Dual Capture Using Floating Diffusion Capacitor

A wide dynamic range CMOS image sensor with dual capture using floating diffusion capacitor is proposed. The proposed structure performs dual capture without on-chip frame memories and the dynamic range of the proposed image sensor is controllable with dual electronic rolling shutter. The chip includes 320 (H) × 240 (V) effective pixels. Each pixel has 33% fill factor in an area of 5.6 × 5.6 μm. The measurement results show that the dynamic range can be maximally expanded by 48 dB and the leakage voltage of the floating diffusion is 27% of the conventional contact metallization. The core area is 3000×2700 μm and total power consumption of the system is 12 mW with 3.3-V analog and 1.8-V digital supply voltages.
Curvelet based adaptive watermarking for images

In this paper, we propose a curvelet based adaptive watermarking for images (CvAWI). A image is firstly transformed into curvelet domain; the watermark is embedded into the curvelet coefficients of the middle-frequency subbands of image lastly. On the one hand, the embedded watermark is well invisible because contrast sensitivity function and watermark visual mask are adopted in our CvAWI. On the other hand, the embedded watermark is very robust due to the spread specialty of curvelet transform (CvT). The corresponding watermarking detection algorithm is proposed to decide whether the watermark is present or not by exploiting the unique transform structure of curvelet. Experimental results show the validity of CvAWI in terms of both watermarking invisibility and watermarking robustness.
TRACKING The importance of score normalization

Generative unigram language models have proven to be a simple though effective model for information retrieval tasks. In contrast to ad-hoc retrieval, topic tracking requires that matching scores are comparable across topics. Several ranking functions based on generative language models: straight likelihood, likelihood ratio, normalized likelihood ratio, and the related Kullback-Leibler divergence are evaluated in two orientations. Best performance is achieved by the models based on a normalized log-likelihood ratio. Key component of these models is the a-priori probability of a story with respect to a common reference dis-
Learning to rank in XML information retrieval: Which feature improve the best?

The augmented adoption of XML as the standard format for representing a document structure requires the development of tools to retrieve and rank effectively elements of the XML documents. It's known that in information retrieval, considering multiple sources of relevance improves information retrieval. In this work some relevance features are defined and used in a learning to rank approach for XML information retrieval. Our aim is to combine theses features to derive good ranking function and show the impact of each feature in the relevance of XML element. Experiments on a large collection from the XML Information Retrieval evaluation campaign (INEX) showed good performance of the approach.
Doppler Sonography Confirmation in Patients Showing Calcified Carotid Artery Atheroma in Panoramic Radiography and Evaluation of Related Risk Factors

Background and aims The purpose of this study was to identify patients at the risk of cerebrovascular attack (CVA) by detecting calcified carotid artery atheroma (CCAA) in panoramic radiography and evaluating their risk factors. Materials and methods A total of 960 panoramic radiographs of patients above 40 years old were evaluated. Doppler Sonography (DS) was performed for patients who showed calcified carotid artery atheroma (CCAA) in panoramic radiogra-phy in order to determine the presence of CCAA and the degree of stenosis. Cardiovascular risk factors in both groups of patients with CCAA (12 subjects) and without CCAA (3 subjects) were compared using a questionnaire filled out by the patients. Statistical analysis including Fisher and independent t-test applied for data analysis. Results Fifteen patients (30 sides) showed calcification in their panoramic radiographs, and underwent DS which revealed CCAA in 16 sides (12 patients). Two patients (13.33%) showed stenosis greater than 70%. Among the risk factors, only age showed a significant association with the occurrence of carotid calcified atheroma (P=0.026). Conclusion Considering the results, dentists should refer especially elderly patients with radiographically identified atheromas for further examinations, as asymptomatic CCAA might be associated with high degrees of stenosis.
Analyzing the Learning Process in Online Educational Game Design: A Case Study

Educational game design environments are used for teaching computational thinking and software engineering concepts to novices. In software engineering education, there has recently been calls for "innovative methods for software teaching and training in online courses" (http://2014.icse-conferences.org/seet). However, to date, learning these concepts is tied to a formal learning environment and the presence of a teacher. In line with the new educational opportunities provided by the Web such as massive open online courses (MOOCs) and e-learning 2.0 platforms, we have created an online educational game design environment with integrated learning resources including video tutorials, showcases, and communication tools. To understand the effect of online educational game design environments with integrated support for learning on novices' use of the system and their learning, we conducted a mixed-method study with nine participants. While the learning goals were achieved to a high degree, the analysis of participants' interaction with the system reveals interesting phenomena about user preferences, such as the fact that during the given computational thinking tasks, the participants preferred the synchronous communication channel to other forms of commonly provided learning resources such as forums.