Miracle-dz/UniFAR-FTUs huggingface dataset:
Pixie: An Operating System for Resource-Aware Programming of Embedded Sensors

A growing class of sensor network applications require high data rates and computationally-intensive node-level processing. When deployed into environments where resources are limited and variable, achieving good performance requires applications to adjust their behavior as resource availability changes. This paper presents Pixie, a new sensor network operating system designed to facilitate the design of highly-efficient resourceaware applications. By allowing applications to introspect on resource availability and providing a rich interface for controlling resource usage, Pixie enables a broad range of adaptation policies through a small set of core abstractions.
Realizing the leasing concept in CORBA-based applications

Although in recent years the concept of leasing has become more and more popular in the field of distributed object-oriented systems, it has not yet been incorporated in the current specification of the widespread CORBA standard.In this paper, we present an approach to the problem of realizing the leasing concept in a CORBA environment. We examine different leasing variants, such as exclusive or shared leasing, leasing with or without prolongation, etc., describe different ways to implement these variants in CORBA, e.g., based on a specialized object adapter, a runtime library or framework, or a CORBA service, and we discuss the advantages and disadvantages of each approach.Furthermore, we show how existing CORBAservices such as Naming, Trading, and Property Service can benefit from leasing facilities. Finally, we briefly present some architectural details of our own implementation of an exemplary CORBA Leasing Service.
A Novel High Breakdown M-estimator for Visual Data Segmentation

Most robust estimators, designed to solve computer vision problems, use random sampling to optimize their objective functions. Since the random sampling process is patently blind and computationally cumbersome, other searches of parameter space using techniques such as Nelder Meade simplex or gradient search techniques have been also proposed (particularly in combination with PbM-estimators). In this paper, we introduce a novel high breakdown M-estimator having a differentiable objective function for which a closed form updating formula is mathematically derived (similar to redescending M-estimators) and used to search the parameter space. The resulting M-estimator has a high breakdown point and is called high breakdown M-estimator (HBM). We show that this objective function can be optimized using an iterative reweighted least squares regression similar to redescending M-estimators. The closed mathematical form of HBM and its guaranteed stability combined with its high breakdown point and fast convergence speed make this estimator an outstanding choice for segmentation of multi-structural data. A number of experiments, using both synthetic and real data have been conducted to show and benchmark the performance of the proposed estimator both in terms of accurate segmentation of numerous structures in the data and also the convergence speed. Moreover, the computational time of HBM, ASSC, MSSE and PbM are compared using the same computing platform and the results show that HBM significantly outperforms aforementioned techniques.
Imperatives and Challenges of Resource Description and Access (RDA) Implementation in Libraries in a Developing Country

ABSTRACT In recent times, information products are becoming more often packaged and repackaged in electronic formats. Cataloguing these resources is a core responsibility of libraries and librarians. A major emerging standardization tool for cataloguing electronic resources is the “resource description and access” (RDA). This article examined the imperatives of RDA implementation among libraries in a developing country—Nigeria. It reviewed global trends and observed the poor RDA implementation level among libraries in Nigeria. It also identified challenges of RDA implementation peculiar to a developing country like Nigeria. The article further outlined strategies that would help to deal with the identified challenges. It concluded that the implementation of RDA is not optional, but mandatory for Nigerian libraries if they must be and remain relevant in the competitive world of librarianship.
Secure Cloud Services: Matrix Multiplication Revisited

This paper revisits the issue of secure outsourcing of services. It proposes a data scrambling approach of transforming private data into public without public key encryption in the cloud computing context. The approach is applicable to non-ordering integer data type used in matrix multiplication. The paper applies row-column shuffling to matrices, and adds random noises to data in order to hide actual values and their sequence. The paper demonstrates the applicability of the approach with a running example.
Araneus in the Era of XML

A large body of research has been recently motivated by the at emp to extend database manipulation techniques to data on the Web (see [13] for a survey). Most of these resear ch efforts – which range from the definition of Web query languages and the related optimizations, to sys tems for Web site development and management, and to integration techniques – started before XML was intro duced, and therefore have strived for a long time to handle the highly heterogeneous nature of HTML pages. In t he meanwhile, Web data sources have evolved from small, home-made collections of HTML pages into comple x platforms for distributed data access and application development, and XML promises to impose itself as a more appropriate format for this new breed of Web sites. XML brings data on the Web closer to databases, s ince, differently from HTML, it is based on a clean distinction between the way the data, its logical str uctu e (the DTD), and the chosen presentation (the stylesheet) are specified. By virtue of this, most of the earl y research proposals for data management on the Web are now being reconsidered in this new perspective (see, for a c llection of references, [4]). In this paper, we discuss the impact of XML on the research wor k c nducted in the last few years by our group in the framework of the A RANEUS project. ARANEUS started as an attempt to investigate the chances of re-applying traditional database concepts and abstractio ns, such as the ones of data-model and query language, to data on the Web. In this spirit, we have developed several t ools and techniques to handle both structured and semistructured data, in the Web style, as follows: (i) a data model calledADM for modeling Web documents and hypertexts [8];(ii) languages for wrapping [12, 16, 14] and querying [8, 17] Web s ites; (iii) tools and techniques for Web site design [9] and implementation [18]. An interesting question is how these tools and applications will work in the era of XML. In the following sections, we will try to answer to this question, by emphasiz ing how XML fits in this framework, how it is influencing our ideas and our way of thinking about Web data so urces, and the design choices needed to adapt our tools – originally conceived for HTML – to the management of XML data. However, a word of caution is needed here: although very popular, XML is still a new propos al, and there are very little (if any) real XMLbased applications on the Web. It is therefore quite difficul t to reason about the data management problems that will come with XML, since we still haven’t experienced them. Because of this, our development will be mainly informal; we will basically try to discuss some of the choice s and tradeoffs related to modeling (in Section 2), querying (in Section 3), and developing (in Section 4) XML da ta sources.
Extracting 3D Trajectories of Objects from 2D Videos using Particle Filter

Depth estimation is a method to estimate the depth information in a 2D image/video, where the original 3D space is projected onto an image plane. This paper introduces a novel extension of depth estimation in the video domain, where we extract 3D trajectories which individually represent the transition of an object in the 3D space. Such 3D trajectories are useful for appropriately characterising spatio-temporal object relations for video event detection. While we extract 3D trajectories by combining depth estimation and object detection results, the major problem is the inconsistency between these results. For example, significantly different depths may be estimated for the region of the same object, and an object region that is appropriately shaped by estimated depths may be missed. To overcome this, we first initialise the 3D position of an object using the frame with the highest consistency between the depth estimation and object detection results. Then, we track the object in the 3D space using particle filter, where a 3D position of the object is modelled as a hidden state to generate its 2D visual appearance. Experimental results demonstrate the effectiveness of our method.
Plant Disease Detection Using Leaf Pattern : A Review

In this review paper we discuss the various methodologies for plant disease detection. Studies show that relying on pure naked-eye observation of experts to detect and classify diseases can be time consuming and expensive, especially in rural areas and developing countries. So we present fast, automatic, cheap and accurate image processing based solution. Solution is composed of four main phases; in the first phase we create a color transformation structure for the RGB leaf image and then, we apply color space transformation for the color transformation structure. Next, in the second phase, the images are segmented using the K-means clustering technique. In the third phase, we calculate the texture features for the segmented infected objects. Finally, in the fourth phase the extracted features are passed through a pre-trained neural network. Keywords-Disease detection, symptoms, neural network, color cooccurrence, plant leaf disease, K-means Method.
Establishment and Validation of ALPH-Q Score to Predict Mortality Risk in Patients With Acute-on-Chronic Hepatitis B Liver Failure

Abstract Currently, there are no robust models for predicting the outcome of acute-on-chronic hepatitis B liver failure (ACHBLF). We aimed to establish and validate a new prognostic scoring system, named ALPH-Q, that integrates electrocardiography parameters that may be used to predict short-term mortality of patients with ACHBLF. Two hundred fourteen patients were included in this study. The APLH-Q score was constructed by Cox proportional hazard regression analysis and was validated in an independent patient cohort. The area under the receiver-operating characteristic curve was used to compare the performance of different models, including APLH-Q, Child–Pugh score (CPS), model of end-stage liver disease (MELD), and a previously reported logistic regression model (LRM). The APLH-Q score was constructed with 5 independent risk factors, including age (HR = 1.034, 95% CI: 1.007–1.061), liver cirrhosis (HR = 2.753, 95% CI: 1.366–5.548), prothrombin time (HR = 1.031, 95% CI: 1.002–1.062), hepatic encephalopathy (HR = 2.703, 95% CI: 1.630–4.480), and QTc (HR = 1.008, 95% CI: 1.001–1.016). The performance of the ALPH-Q score was significantly better than that of MELD and CPS in both the training (0.896 vs 0.712, 0.896 vs 0.738, respectively, both P < 0.05) and validation cohorts (0.837 vs 0.689, 0.837 vs 0.585, respectively, both P < 0.05). Compared with LRM, APLH-Q also showed a better performance (0.896 vs 0.825, 0.837 vs 0.818, respectively). We have developed a novel APLH-Q score with greater performance than CPS, MELD, and LRM for predicting short-term mortality of patients with ACHBLF.
Optimum resource allocation in OFDM systems using frbs and Particle Swarm Optimization

Optimum allocation of resources is demand of every modern communication system. In this way the transmission parameters like code rate, modulation size and power are optimally chosen to enhance the overall system throughput while satisfying certain constraints at the same time. A similar constrained optimization problem is focused for OFDM environment and solved by proposed Fuzzy Rule Based System (FRBS) and Particle Swarm Optimization (PSO). In this proposal FRBS is used for adapting code rate and modulation size while PSO is used for adaptive power. Significance of the proposed scheme is shown through the simulations.
Maximum likelihood estimation for semiparametric transformation models with interval-censored data

Abstract Interval censoring arises frequently in clinical, epidemiological, financial and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasses proportional hazards and proportional odds models. We consider nonparametric maximum likelihood estimation for this class of models with an arbitrary number of monitoring times for each subject. We devise an EM-type algorithm that converges stably, even in the presence of time-dependent covariates, and show that the estimators for the regression parameters are consistent, asymptotically normal, and asymptotically efficient with an easily estimated covariance matrix. Finally, we demonstrate the performance of our procedures through simulation studies and application to an HIV/AIDS study conducted in Thailand.
Issues in Validating Object-Oriented Metrics for Early Risk Prediction

A large number of object-oriented class metrics have been proposed over the last decade, for example [1][3][5]. The basic premise behind the development of object-oriented metrics is that they can serve as early predictors of classes that contain faults (found during testing or that result in field failures) or that are costly to maintain. A growing body of work has attempted to empirically validate this premise, for example [2][3].
Methods Assessment the Probability Density of Discrete Signals in Telecommunications

This paper is devoted to investigation of problems and methods of acoustic signals modeling in the information and control systems for audio exchange communications. The problems of estimation and approximation of probable density functions, which may assist in distinction of acoustic speech signals and external acoustic noise. We consider the direct and indirect methods, techniques histogram evaluation, ways to overcome incorrect problems.
Analysis and defense of vulnerabilities in binary code

In this thesis, we develop techniques for vulnerability analysis and defense that only require access to vulnerable programs in binary form. Our approach does not use or require source code. We focus on a binary-centric approach since everyone typically has access to the binary code for the programs they run. Thus, our approach is applicable to a wider audience than previous approaches that require or utilize source code. In addition, the binary itself is often the most faithful encoding of security-relevant details since it is what is actually executed on hardware. In order to demonstrate the benefits of binary-centric vulnerability analysis and defense, we first develop binary analysis techniques. We have implemented our techniques as part of a binary analysis architecture called Vine. We then demonstrate the utility of our approach, and Vine, in two typical applications of vulnerability analysis and defense. First, we develop binary analysis techniques for reverse engineering a patched vulnerability. More specifically, our techniques enable an attacker to reverse engineer exploits from software patches that fix program bugs and vulnerabilities. We call this automatic patch-based exploit generation. We demonstrate automatic patch-based exploit generation on real vulnerabilities using Vine. In our experiments, it only takes a few minutes to generate an exploit from the patched program. We argue one consequence of our results is that current delayed patch distribution architectures (e.g., Windows Automatic Update) may hurt security. Second, we propose methods and techniques for generating input filters based upon vulnerability analysis. An input filter is a recognizer for inputs that exploit a vulnerability. We develop the first automatic techniques for generating input filters with accuracy guarantees even when there may be restrictions on the input filtering language. We demonstrate our techniques by automatically generating input filters from vulnerable binary programs.
Intrinsic Breaking of Internal Solitary Waves in a Deep Lake

Based on simulations with the Dubreil-Jacotin-Long (DJL) equation, the limiting amplitude and the breaking mechanisms of internal solitary waves of depression (ISWs) are predicted for different background stratifications. These theoretical predictions are compared to the amplitude and the stability of the leading internal solitary waves of more than 200 trains of ISWs observed in the centre of a sub-basin of Lake Constance. The comparison of the model results with the field observations indicates that the simulated limiting amplitude of the ISWs provides an excellent prediction of the critical wave height above which ISWs break in the field. Shear instabilities and convective instabilities are each responsible for about half of the predicted wave breaking events. The data suggest the presence of core-like structures within the convectively unstable waves, but fully developed and stable cores were not observed. The lack of stable trapped cores in the field can be explained by the results from dynamic simulations of ISWs with trapped cores which demonstrate that even slight disturbances of the background stratification cause trapped cores to become unstable.
A Cloud Computing System to Quickly Implement New Microarray Data Pre-processing Methods

Background: Pre-processing, including normalization of raw microarray data is crucial to microarray-related data analysis. It takes time and effort to build newly-developed algorithms into commercial software or locally developed systems. While most new algorithms emerge in the form of sharable R packages, it can be difficult for many biologists to apply them as soon as they are available. Currently, we rely on statisticians and experienced programmers to develop and implement code to access those R packages. Therefore, we need a robust procedure to quickly implement pre-processing methods as they appear. The newly emerging cloud computing concept has directed us toward a new way for providing an easily accessible service to the biologists without requiring them to have any programming knowledge in R. Results: Based on our earlier Java-based software tool JavaStat, we developed an internet based application prototype to upload data and carry out pre-processing applications that include normalization, statistical analyses and plots. More im- portantly, R packages, e. g. , for newly-developed normalization methods, and GC-robust multichip algorithm (RMA) for exon arrays, can be easily incorporated into the system with limited inputs from a biologist or a programmer. The data are stored in the cloud and the R code runs on server. Conclusion: The newly emerged cloud computing concept provides us a new way to provide an easily accessible and up- to-date service to biologists, as evidenced by our JavaStat system to incorporate new pre-processing package as they ap- pear. Users can access the application with a newly incorporated module through the Web. We expect this and other simi- lar systems greatly decrease turn-around time, improve accessibility of newly developed R model for pre-processing algo- rithms.
Genetic Network Programming with Reinforcement Learning and Its Application to Creating Stock Trading Rules

Evolutionary Computation is well-known for producing the solutions in optimization problems based on change, composition and selection. We have proposed Genetic Network Programming (GNP) [1, 2] as an extended method of Genetic Algorithm (GA) [3, 4] and Genetic Programming (GP) [5, 6]. It has been clarified that GNP is an effective method mainly for dynamic problems since GNP represents its solutions using graph structures, which contributes to creating quite compact programs and implicitly memorizing past action sequences in the network flows. Moreover, we proposed an extended algorithm of GNP which combines evolution and reinforcement learning [7] (GNP-RL). GNP-RL has two advantages, and one of them is online learning. Since original GNP is based on evolution only, the programs are evolved mainly after task execution or enough trial, i.e., offline learning. On the other hand, the programs in GNP-RL can be changed incrementally based on rewards obtained during task execution, i.e., online learning. Concretely speaking, when an agent takes a good action with a positive reward at a certain state, the action is reinforced and when visiting the state again, the same action will be adopted with higher probability. Another advantage of GNP-RL is the combination of a diversified search of GNP and an intensified search of RL. The role of evolution is to make rough structures through selection, crossover and mutation, while the role of RL is to determine one appropriate path in a structure made by evolution. Diversified search of evolution could change programs largely with which the programs could escape from local minima. RL is executed based on immediate rewards obtained after taking actions, therefore intensified search can be executed efficiently. Research on stock price prediction and trading model using evolutionary computation and neural networks has been done [8–10] in recent years. Generally speaking, there are two kinds of methods for predicting stock prices and determining the timing of buying or selling stocks: one is fundamental analysis which analyzes stock prices using the financial statement of each company, the economic trend and movements of the exchange rate; the other is technical analysis which analyzes numerically the past movement of stock prices. The proposed method belongs to technical analysis since it determines the timing of buying and selling stocks based on the technical indices such as Relative Strength Index, MACD, Golden/Dead Cross and so on. O pe n A cc es s D at ab as e w w w . in te ch w eb . o rg
Navigating the Semantic Horizon using Relative Neighborhood Graphs

This paper is concerned with nearest neighbor search in distributional semantic models. A normal nearest neighbor search only returns a ranked list of neighbors, with no information about the structure or topology of the local neighborhood. This is a potentially serious shortcoming of the mode of querying a distributional semantic model, since a ranked list of neighbors may conflate several different senses. We argue that the topology of neighborhoods in semantic space provides important information about the different senses of terms, and that such topological structures can be used for word-sense induction. We also argue that the topology of the neighborhoods in semantic space can be used to determine the semantic horizon of a point, which we define as the set of neighbors that have a direct connection to the point. We introduce relative neighborhood graphs as method to uncover the topological properties of neighborhoods in semantic models. We also provide examples of relative neighborhood graphs for three well-known semantic models; the PMI model, the GloVe model, and the skipgram model.