Reviewerly/exHarmony huggingface dataset:
Parallel Object Compositions for the Search of Sequences DNA Strings in the Construction of Gnomes

Within an environment of parallel objects, an approach of structured parallel programming and the paradigm of the orientation to objects show a programming method based on high level parallel compositions or HLPCs to solve two problems of combinatorial optimization: grouping fragments of DNA sequences and the parallel exhaustive search (PES) of RNA strings that help the sequence and the assembly of DNAs. The pipeline and farm models are shown as HLPCs under the object orientation paradigm and with them it is proposed the creation of a new HLPCs that combines and uses the previous ones to solve the cited problems. Each HLPC proposal contains a set of predefined synchronization constraints between processes, as well as the use of synchronous, asynchronous and asynchronous future modes of communication. This article shows the algorithms that solve the problems, their design and implementation as HLPCs and the performance metrics in their parallel execution using multicores and video accelerator card.
Pre-Processing Images of Public Signage for OCR Conversion

In this paper, we propose a novel method to enhance the OCR (Optical Character Recognition) readability of public signboards captured by smart-phone cameras-both outdoors and indoors, and subject to various lighting conditions.A distinct feature of our technique is the detection of these signs in the HSV (Hue, Saturation and Value) color space, done in order to filter out the signboard from the background, and correctly interpret the textual details of each signboard.This is then binarized using a thresholding technique that is optimized for text printed on contrasting backgrounds, and passed through the Tesseract engine to detect individual characters.We test out our technique on a dataset of over 200 images taken in and around the campus of our college, and are successful in attaining better OCR results in comparison to traditional methods.Further, we suggest the utilization of a method to automatically assign ROIs (Regions Of Interest) to detected signboards, for better recognition of textual information.
Construction and Reduction Methods of Web Spam Identification Index System

Background: With the rapid development of the Internet, the number of web spam has increased dramatically in recent years, which has wasted search engine storage and computing power on a massive scale. To identify the web spam effectively, the content features, link features, hidden features and quality features of web page are integrated to establish the corresponding web spam identification index system. However, the index system is highly correlation dimension. Methods: An improved method of autoencoder named stacked autoencoder neural network (SAE) is used to realize the reduction of the web spam identification index system. Results: The experiment results show that our method could reduce effectively the index of web spam and significantly improves the recognition rate in the following work. Conclusion: An autoencoder based web spam indexes reduction method is proposed in this paper. The experimental results show that it greatly reduces the temporal and spatial complexity of the future web spam detection model.
Commutative Watermarking-Encryption of Multimedia Data Based on Histograms

Histogram-based watermarking schemes are invariant to pixel permutations and can thus be combined with permutation-based ciphers to form a commutative watermarking-encryption scheme. In this chapter, the authors demonstrate the feasibility of this approach for audio data and still image data. Typical histogram-based watermarking schemes based on comparison of histogram bins are prone to desynchronization attacks, where the whole histogram is shifted by a certain amount. These kind of attacks can be avoided by synchronizing the embedding and detection processes, using the mean of the histogram as a calibration point. The resulting watermarking scheme is resistant to three common types of shifts of the histogram, while the advantages of previous histogram-based schemes, especially commutativity of watermarking and permutation-based encryption, are preserved. The authors also report on the results of testing robustness of the still image watermark against JPEG and JPEG2000 compression and on the possibility of using histogram-based watermarks for authenticating the content of an image.
Group approach to solving the tasks of recognition

In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. Probabilistic model of classification with use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.
Digraphs associated with the kth power map on the quotient ring of polynomials over finite fields

Let [Formula: see text] be a monic polynomial over a finite field [Formula: see text], and [Formula: see text] an integer. A digraph [Formula: see text] is one whose vertex set is [Formula: see text] and for which there is a directed edge from a polynomial [Formula: see text] to [Formula: see text] if [Formula: see text] in [Formula: see text]. If [Formula: see text], where the [Formula: see text]’s are distinct monic irreducible polynomials over [Formula: see text], then [Formula: see text] can be factorized as [Formula: see text]. In this work, we investigate the structure of these power digraphs. The semiregularity property is examined, and its relationship with the symmetric property is established. In addition, we look into the uniqueness of factorization of trees attached to a fixed point.
Optimization of selective ensemble for cost-sensitive classification: An empirical study

Cost-sensitive classification is broadly investigated in many real-life decision-making applications where the different misclassification errors may cause asymmetric costs. However, the cost values are usually hardly specified in practice for decision makers especially when they are faced with the complex multi-class decision problems. In this paper we attempt to take advantage of the pairwise comparisons originally proposed in Analytic Hierarchy Process (AHP) and offer decision makers a flexible, qualitative manner for cost specification rather than the definite, quantitative cost assignment. The cost ratios associated with the classes are then derived from the comparison matrix and used as the parameter of the subsequent cost-sensitive classifiers. To promote the performance of single classifier we construct the ensembles of multiple cost-sensitive Learning Vector Quantization Neural Networks (LVQ-NNs) trained independently and then combined together by various weighted voting approaches. Empirical study on some real-world databases reveal that the well optimized cost-sensitive ensembles based on evolutionary computing approaches perform significantly better than the best single classifier within the ensemble in terms of generalization ability and stability.
Space Planning and Off-Site Storage

Nontraditional library services for scientists are gaining traction, but one overlooked “service” is aimed at institutional administration—offering existing and traditional library activities at reduced cost. Efficiencies brought by technology now allow acquisitions, collections management, and reference service to be done more cheaply than in the past. For historical reasons, the library often occupies a central space at research organizations, and that space is becoming more valuable to management for other purposes. Many libraries have been moved either to nearby buildings or to off-site storage facilities in recent years, and while this may be disappointing, it seems inevitable. Science libraries are particularly vulnerable due to the journal-centric nature of their collections but may be able to leverage support for nontraditional library services in exchange for cooperation in moving collections. This chapter discusses some of the issues around moving science libraries and the technology and communication strategies that can make the disruption somewhat more palatable to all involved. Books about library-space planning and moving collections are available which provide great detail on all the issues. This chapter is instead intended to provide ideas and guidance on how science libraries are affected.
A context aware model for autonomous agent stochastic planning

Markov Decision Processes (MDPs) are not able to make use of domain information effectively due to their representational limitations. The lacking of elements which enable the models be aware of context, leads to unstructured representation of that problem such as raw probability matrices or lists. This causes these tools significantly less efficient at determining a useful policy as the state space of a task grows, which is the case for more realistic problems having localized dependencies between states and actions. In this paper, we present a new state machine, called Context-Aware Markov Decision Process (CA-MDP) based on MDP for the purpose of representing Markovian sequential decision making problems in a more structured manner. CA-MDP changes and augments MDP facilities by integrating causal relationships between actions and states thereby enabling structural, hence compact if possible, representation of the tasks. To show the expressive power of CA-MDP, we give the theoretical bounds for complexity of conversion between MDP and CA-MDP to demonstrate the expressive power of CA-MDP. Next, to generate an optimal policy from CA-MDP encoding by exploiting those newly defined facilities, we devised a new solver algorithm based on value iteration (VI), called Context-Aware Value Iteration (CA-VI). Although regular dynamic programming (DP) based algorithms is successful at effectively determining optimal policies, they do not scale well with respect to state–action space, making both the MDP encoding and related solver mechanism practically unusable for real-life problems. Our solver algorithm gets the power of overcoming the scalability problem by integrating the structural information provided in CA-MDP. First, we give theoretical analysis of CA-VI by examining the expected number of Bellman updates being performed on arbitrary tasks. Finally, we present our conducted experiments on numerous problems, with important remarks and discussions on certain aspects of CA-VI and CA-MDP, to justify our theoretical analyses empirically and to assess the real performance of CA-VI with CA-MDP formulation by analysing the execution time by checking how close it gets to the practical minimum runtime bound with respect to VI performance with MDP encoding of the same task.
A Study on the Performance and Scalability of Apache Flink Over Hadoop MapReduce

With the advancements in science and technology, data is being generated at a staggering rate. The raw data generated is generally of high value and may conceal important information with the potential to solve several real-world problems. In order to extract this information, the raw data available must be processed and analysed efficiently. It has however been observed, that such raw data is generated at a rate faster than it can be processed by traditional methods. This has led to the emergence of the popular parallel processing programming model – MapReduce. In this study, the authors perform a comparative analysis of two popular data processing engines – Apache Flink and Hadoop MapReduce. The analysis is based on the parameters of scalability, reliability and efficiency. The results reveal that Flink unambiguously outperformance Hadoop's MapReduce. Flink's edge over MapReduce can be attributed to following features – Active Memory Management, Dataflow Pipelining and an Inline Optimizer. It can be concluded that as the complexity and magnitude of real time raw data is continuously increasing, it is essential to explore newer platforms that are adequately and efficiently capable of processing such data.
Fully Reflective Execution Environments: Virtual Machines for More Flexible Software

VMs are complex pieces of software that implement programming language semantics in an efficient, portable, and secure way. Unfortunately, mainstream VMs provide applications with few mechanisms to alter execution semantics or memory management at run time. We argue that this limits the evolvability and maintainability of running systems for both, the application domain, e.g., to support unforeseen requirements, and the VM domain, e.g., to modify the organization of objects in memory. This work explores the idea of incorporating reflective capabilities into the VM domain and analyzes its impact in the context of software adaptation tasks. We characterize the notion of a fully reflective VM, a kind of VM that provides means for its own observability and modifiability at run time. This enables programming languages to adapt the underlying VM to changing requirements. We propose a reference architecture for such VMs and present TruffleMATE as a prototype for this architecture. We evaluate the mechanisms TruffleMATE provides to deal with unanticipated dynamic adaptation scenarios for security, optimization, and profiling aspects. In contrast to existing alternatives, we observe that TruffleMATE is able to handle all scenarios, using less than 50 lines of code for each, and without interfering with the application's logic.
A Frequency Scaling Based Performance Indicator Framework for Big Data Systems

It is important for big data systems to identify their performance bottleneck. However, the popular indicators such as resource utilizations, are often misleading and incomparable with each other. In this paper, a novel indicator framework which can directly compare the impact of different indicators with each other is proposed to identify and analyze the performance bottleneck efficiently. A methodology which can construct the indicator from the performance change with the CPU frequency scaling is described. Spark is used as an example of a big data system and two typical SQL benchmarks are used as the workloads to evaluate the proposed method. Experimental results show that the proposed method is accurate compared with the resource utilization method and easy to implement compared with the white-box method. Meanwhile, the analysis with our indicators leads to some interesting findings and valuable performance optimization suggestions for big data systems.
Feature Points Selection for Rectangle Panorama Stitching

Panoramic images can provide users with large view scene, which is widely used in various fields. Image stitching can combine images of adjacent views with small horizon field into a single image with large horizon. Currently stitching method can provide a rectangle panorama by cropped method to view or print. However, this method can occur shape distortion, and information loss. In this paper, we propose a novel feature points selection method to generate rectangle panorama image. First, to avoid local distortion in overlapping region, matched feature points are selected by feature cluster analysis. And then, depending on selected feature points, we establish mesh warping method to produce rectangle mesh. At last, bilinear interpolation algorithm is used to obtain the rectangle panorama image. Experimental results show that the proposed method can effectively stitch different view image to generate rectangle panorama without shape distortion.
A Clustering Approach Based on Cooperative Agents to Improve Decision Support in ERP

Multi-agent systems (MAS) are a powerful technology for the design and implementation of autonomous intelligent systems that can handle distributed problem solving in a complex environment. This technology has played an important role in the development of data mining systems in the last decade, the purpose of which is to promote the extraction of information and knowledge from a large database and to make these systems more scalable. In this chapter, the authors present a clustering system based on cooperative agents through a centralized and common ERP database to improve decision support in ERP systems. To achieve this, they use multi-agent system paradigm to distribute the complexity of k-means algorithm in several autonomous entities called agents, whose goal is to group records or observations on similar objects classes. This will help business decision makers to make good decisions and provide a very good response time by the use of the multi-agent system. To implement the proposed architecture, it is more convenient to use the JADE platform while providing a complete set of services and have agents comply with the specifications FIPA.
Discovery of Characteristic Sequential Patterns Based on Two Types of Constraints

This article proposes a method for discovering characteristic sequential patterns from sequential data by using background knowledge. In the case of the tabular structured data, each item is composed of an attribute and an attribute value. This article focuses on two types of constraints describing background knowledge. The first one is time constraints. It can flexibly describe relationships related to the time between items. The second one is item constraints, it can select items included in sequential patterns. These constraints can represent the background knowledge representing the interests of analysts. Therefore, they can easily discover sequential patterns coinciding the interests as characteristic sequential patterns. Lastly, this article verifies the effect of the pattern discovery method based on both the evaluation criteria of sequential patterns and the background knowledge. The method can be applied to the analysis of the healthcare data.
Research-Information Management

Collecting and highlighting an institution’s research outputs is a service in which science librarians are becoming increasingly involved. This includes collecting reprints of publications authored by scientists at their institution as well as documenting data deposited, grants awarded, research facilities and labs, and other components of a standard research portfolio. In recent years, commercial services have been introduced to facilitate this collection of data which serves both scientists and (perhaps more importantly) institution administration and research evaluation groups. In countries where comprehensive research assessment takes place, research information system helps to deliver comprehensive, consistent, and measurable data source. But these systems have found a wide variety of audiences including communications and public-information offices. Several familiar library vendors now offer services to collect and report standard research activity, and the movement into this product line by these familiar vendors indicates that librarians are in a position to be a core participant.
Estimating of the inertial manifold dimension for a chaotic attractor of complex Ginzburg-Landau equation using a neural network

Dimension of an inertial manifold for a chaotic attractor of spatially distributed system is estimated using autoencoder neural network. The inertial manifold is a low dimensional manifold where the chaotic attractor is embedded. The autoencoder maps system state vectors onto themselves letting them pass through an inner state with a reduced dimension. The training processes of the autoencoder is shown to depend dramatically on the reduced dimension: a learning curve saturates when the dimension is too small and decays if it is sufficient for a lossless information transfer. The smallest sufficient value is considered as a dimension of the inertial manifold, and the autoencoder implements a mapping onto the inertial manifold and back. The correctness of the computed dimension is confirmed by its remarkable coincidence with the one obtained as a number of covariant Lyapunov vectors with vanishing pairwise angles. These vectors are called physical modes. Unlike never having zero angles residual ones they are known to span a tangent subspace for the inertial manifold.
SeFM: A Sequential Feature Point Matching Algorithm for Object 3D Reconstruction

3D reconstruction is a fundamental issue in many applications, and the feature point matching problem is a key step while reconstructing target objects. Conventional algorithms can only find a small number of feature points from two images which is quite insufficient for reconstruction. To overcome this problem, we propose SeFM a sequential feature point matching algorithm. We first utilize the epipolar geometry to find the epipole of each image. Rotating along the epipole, we generate a set of the epipolar lines and reserve those intersecting with the input image. Next, a rough matching phase, followed by a dense matching phase, is applied to find the matching dot-pairs using dynamic programming. Furthermore, we also remove wrong matching dot-pairs by calculating the validity. Experimental results illustrate that SeFM can achieve around 1000–10,000 times matching dot-pairs, depending on individual image, compared to conventional algorithms, the object reconstruction with only two images is semantically visible. Moreover, it outperforms conventional algorithms, such as SIFT and SURF, regarding precision and recall.
Deep Energies for Estimating Three-Dimensional Facial Pose and Expression

While much progress has been made in capturing high-quality facial performances using motion capture markers and shape-from-shading, high-end systems typically also rely on rotoscope curves hand-drawn on the image. These curves are subjective and difficult to draw consistently; moreover, ad-hoc procedural methods are required for generating matching rotoscope curves on synthetic renders embedded in the optimization used to determine three-dimensional (3D) facial pose and expression. We propose an alternative approach whereby these curves and other keypoints are detected automatically on both the image and the synthetic renders using trained neural networks, eliminating artist subjectivity, and the ad-hoc procedures meant to mimic it. More generally, we propose using machine learning networks to implicitly define deep energies which when minimized using classical optimization techniques lead to 3D facial pose and expression estimation.
On bounded remainder intervals and strongly non-bounded remainder intervals for sequences <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msub><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mo stretchy="false">{</mml:mo><mml:msub><mml:mrow><mml:mi>a</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mi>α</mml:mi><mml:mo stretchy="false">}</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mi>n</mml:mi><…

We give some results on the existence of bounded remainder intervals (BRI) for sequences of the form ({anα})n≥1, where (an)n≥1 – in most cases – is a given sequence of distinct integers. Further we introduce the concept of strongly non-bounded remainder intervals (S-NBRI) and we show for a very general class of polynomial-type sequences that these sequences cannot have any S-NBRI, whereas for the sequence ({2nα})n≥1 every interval is an S-NBRI.