Miracle-dz/UniFAR-FTUs huggingface dataset:
MIDER: Network Inference with Mutual Information Distance and Entropy Reduction

The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information–theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide range of problems without requiring tuning.
Image Segmentation Techniques

Image segmentation is the process of partitioning an image into multiple segments, so as to change the representation of an image into something that is more meaningful and easier to analyze. Several general-purpose algorithms and techniques have been developed for image segmentation. This paper describes the different segmentation techniques used in the field of ultrasound and SAR Image Processing. Firstly this paper investigates and compiles some of the technologies used for image segmentation. Then a bibliographical survey of current segmentation techniques is given in this paper and finally general tendencies in image segmentation are presented. I. Introduction Images are considered as one of the most important medium of conveying information, in the field of computer vision, by understanding images the information extracted from them can be used for other tasks for example: navigation of robots, extracting malign tissues from body scans, detection of cancerous cells, identification of an airport from remote sensing data. Now there is a need of a method, with the help of which, we can understand images and extract information or objects, image segmentation fulfill above requirements. Thus, image segmentation is the first step in image analysis. Some time image denoising is done before the segmentation to avoid from the false contour selection for segmentation to segment the image without loss of information for medical diagnosing purpose is a challenging job. The purpose of writing this paper is to provide a literature review in this arena. The remainder of this paper is organized as below; section II introduces the term image segmentation. Section III describes the current image segmentation techniques and section IV Concludes the overall study.
Selection of Reliable Reference Genes for Gene Expression Studies Using Real-Time PCR in Tung Tree during Seed Development

Quantitative real-time PCR (RT-qPCR) has become an accurate and widely used technique to analyze expression levels of selected genes. It is very necessary to select appropriate reference genes for gene expression normalization. In the present study, we assessed the expression stability of 11 reference genes including eight traditional housekeeping genes and three novel genes in different tissues/organs and developing seeds from four cultivars of tung tree. All 11 reference genes showed a wide range of Ct values in all samples, indicating that they differently expressed. Three softwares – geNorm, NormFinder and BestKeeper – were used to determine the stability of these references except for ALB (2S albumin), which presented a little divergence. The results from the three softwares showed that ACT7 (Actin7a), UBQ (Ubiquitin), GAPDH (glyceraldehyde-3-phosphate dehydrogenase) and EF1α (elongation factor 1-α) were the most stable reference genes across all of the tested tung samples and tung developing seeds, while ALB (2S albumin) was unsuitable as internal controls. ACT7, EF1β (elongation factor1-beta), GAPDH and TEF1 (transcription elongation factor 1) were the top four choices for different tissues/organs whereas LCR69 did not favor normalization of RT-qPCR in these tissues/organs. Meanwhile, the expression profiles of FAD2 and FADX were realized using stable reference genes. The relative quantification of the FAD2 and FADX genes varied according to the internal controls and the number of internal controls. The results further proved the importance of the choice of reference genes in the tung tree. These stable reference genes will be employed in normalization and quantification of transcript levels in future expression studies of tung genes.
An MDE approach for modeling network on chip topologies

Network on Chip (NoC) is a research field path that primarily addresses the global communication in System on Chip (SoC). The selected topology of the components interconnects plays a prime role in the performance of NoC architecture, for NoC conception, high-level synthesis approaches are utilized thus the behaviorally description of the system is refined into an accurate register-transfer-level (RTL) design for SoC implementation. In the recent MARTE (Modeling and Analysis of Real-time and Embedded Systems) Profile, a notion of multidimensional multiplicity has been proposed to model repetitive structures and topology. This paper presents a contribution for a new methodology for modeling NoC based Model Driven Architecture and the Modeling and Analysis of Real-Time and embedded System (MARTE), it aims to prove the effectiveness of standard MARTE in modeling irregular or globally irregular locally regular architectures. We will start this work by high level abstraction to reach low level through generated VHDL code.
Image segmentation by reaction-diffusion bubbles

Figure-ground segmentation is a fundamental problem in computer vision. The main difficulty is the integration of low-level, pixel-based local image features to obtain global object-based descriptions. Active contours in the form of snakes, balloons, and level-set modeling techniques have been proposed that satisfactorily address this question for certain applications. However, these methods require manual initialization, do not always perform well near sharp protrusions or indentations, or often cross gaps. We propose an approach inspired by these methods and a shock-based representation of shape in terms of parts, protrusions, and bends. Since initially it is not clear where the objects or their parts are, parts are hypothesized in the form of fourth order shocks randomly initialized in homogeneous areas of images. These shocks then form evolving contours, or bubbles, which grow, shrink, merge, split and disappear to capture the objects in the image. In the homogeneous areas of the image bubbles deform by a reaction-diffusion process. In the inhomogeneous areas, indicated by differential properties computed from low-level processes such as edge-detection, texture, optical-flow and stereo, etc., bubbles do not deform. As such, the randomly initialized bubbles integrate low-level information and, in the process, segment the figures from the ground. <<ETX>>
"target Tracking and Interception by Aggressive Honeybees" Target Tracking and Interception by Aggressive Honeybees

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Object-oriented vision for a behavior-based robot

As one realization out of the class of behavior-based robot architectures a specific concept of situation-oriented behavior-based navigation has been proposed. Its main characteristic is that the selection of the behaviors to be executed in each moment is based on a continuous recognition and evaluation of the dynamically changing situation in which the robot is finding itself. An important prerequisite for such as approach is a timely and comprehensive perception of the robot's dynamically changing environment. Object-oriented vision as proposed and successfully applied, e.g., in freeway traffic scenes is a particularly well suited sensing modality for robot control. Our work concentrated on modeling the physical objects which are relevant for indoor navigation, i.e. walls, intersections of corridors, and landmarks. In the interest of efficiency these models include only those necessary features for allowing the robot to reliably recognize different situations in real time. According to the concept of object- oriented vision recognizing such objects is largely reduced to a knowledge-based verification of objects or features that may be expected to be visible in the current situation. The following results have been achieved: 1) By using its vision system and a knowledge base in the form of an attributed topological map the robot could orient itself and navigate autonomously in a known environment. 2) In an unknown environment the robot was able to build, by means of supervised learning, an attributed topological map as a basis for subsequent autonomous navigation. 3) The experiments could be performed both under unmodified artificial light and under natural light shining through the glass walls of the building.
Implementation of Overlay Function Based on Fuzzy Logic in Spatial Decision Support System

3 Abstract: The overlay functions in GIS are well known and often needed tools for integrating different factors and generating useful information for decision makers. Both the integration model and the generated information usually rely on crisp set theory. In many cases, either the boundaries of classes are not clearly defined, or the classification of a feature into a class is not obvious. In such cases, overlay based on fuzzy set and fuzzy logic is unavoidable. The aim of this study is to develop overlay functions on the basis of fuzzy logic and to examine the usability of such functions in integrating data related to indeterminate aspects of a phenomenon. To implement and examine the idea, an application program is developed using VBA programming language and the available Arc Object library. Using this application, a user can generate fuzzy data and use different fuzzy overlay functions to integrate those data. To test the applicability of the model and application, the suitable locations for building commercial sites of oil products are determined using this application. In summery, data integration using fuzzy overlay functions can improve the reliability of decisions, when dealing with indeterminate phenomena.
The detection of multi-objective billiards in snooker game video

In this paper, we proposed a kind of multi-objective detection technique based on watershed in snooker game video. It can automatically extracts target markers without any man-machine interactions. As to target that has similar color with background, we make full use of its reflective region to detect it indirectly which reduces burden of detecting directly. Meanwhile, it can effectively separate gathered targets especially like gathered crimson balls. At first, for the single color of billiard table, we use color segmentation, Hough transform algorithm to extract pool table so as to get rid of the interference of other objects outside the pool table; Second, combine color segmentation with watershed algorithm to extract all non-green objectives; Then according to the relationship of reflective point and the target itself, we can pick up the green one. At last, we use the difference between color segmentation label and number of reflex points of target area to segment all gathered goals. Experiments show that the proposed algorithm can effectively detect multi-objects in snooker game video.
Overtraced strokes to single line drawing: Preparation of sketch for 3D interpretation

This paper presents a novel method for interpreting overtraced strokes to single line drawing. The approach consists of three stages: preprocessing, stroke classification, strokes grouping and fitting. The preprocessing methods include stroke preprocessing and grouping preprocessing. The former discuss the polygonal approximation to represents the stroke and the latter proposes a method to evaluate whether the strokes should be grouped by time interval and distance interval of two adjacent strokes. It is followed by an innovative strokes grouping process that handles polylines and curves separately. This paper proposes a method for rapid fitting overtraced line using the characteristics of stroke recognition result. According to the position relation of strokes recognition result, the two polylines are divided into difference catalogs. The definitions of the rotation angle and the endpoint of generalized stroke of the non-closed conic curve are proposed in this paper. The grouped strokes are fitted with 2D geometry and a complete definition of the fitting geometry of grouped stroke with linetype is proposed. This paper develops a human-computer interface prototype system FSR using the proposed theory. The interface overcomes the limitation where only a single line drawing can be sketched out as in most existing sketching programs, meanwhile is more intuitive and easy to the user. The FRS system has been tested with a number of overtraced and non-overtraced strokes, and the test results show that the algorithm achieves a satisfactory interpretation.
An Analysis of the Effects of Intelligent Location Prediction Algorithms on Greedy Geographic Routing in Mobile Ad-Hoc Networks

Geographic routing uses the physical locations of devices for routing purposes instead of the conventional routing protocols which make use of logical addressing schemes representing an abstract hierarchy. Greedy geographic routing is a popular method favoured for its efficiency and its simplicity that considers only one hop neighbours. Greedy routing needs only minimal network information and as such is resilient to change and dynamic behaviour. Recent advances have seen the development of location prediction algorithms that use a variety of methods to determine a node’s future location based on their previous movements. Such prediction schemes can potentially benefit greedy geographic routing by allowing nodes to make routing decisions based on where a node will go rather than where it was according to the last update. The use of location prediction algorithms therefore allows geographic routing protocols to make decisions that are more intelligent, thus not only improving routing performance but providing a crucial step towards fully autonomous computer communications. The main contribution of this paper is the implementation and analysis of two existing location prediction schemes on top of the existing Greedy Perimeter Stateless Routing (GPSR) protocol ran in greedy mode.
Epsilon-near-zero behavior from plasmonic Dirac point: Theory and realization using two-dimensional materials

The electromagnetic response of a two-dimensional metal embedded in a periodic array of a dielectric host can give rise to a plasmonic Dirac point that emulates Epsilon-Near-Zero (ENZ) behavior. This theoretical result is extremely sensitive to tructural features like periodicity of the dielectric medium and thickness imperfections. We propose that such a device can actually be realized by using graphene as the 2D metal and materials like the layered semiconducting transition-metal dichalcogenides or hexagonal boron nitride as the dielectric host. We propose a systematic approach, in terms of design characteristics, for constructing metamaterials with linear, elliptical and hyperbolic dispersion relations which produce ENZ behavior, normal or negative diffraction.
Algorithm to improve the outage performance of IF Virtual MIMO system

In this paper, we improve the outage performance of a 2 by 2 Virtual MIMO system based on Compute-and-Forward method [1]. The current scheme requires negotiations between the relays and destination to determine which decoded message the relay should transmit. We propose a scheme which requires no cooperation between relay and Central Office, thus reducing the transmission overhead and latency. We show that in many cases our schemes achieve the same performance as the case where decision is made globally.
Controllable Network Architecture Based on SDN

With the size and various applications of Internet growing, the structure and function of the network are becoming more and more complex, which makes it hard to be managed and controlled. The Internet has difficulty in providing QoS guarantees. Therefore, this paper proposes a controllable network architecture based on SDN[1]. In this architecture, "Q+ X" business guarantee mechanism is introduced using the method of resource reservation to provide QoS guarantees for Q business and traditional best-effort service for X business. This paper designs allocation and management module of Q class resource, routing module, flow measurement and monitoring module for Q class business. Finally, in terms of the availability and effectiveness, this paper tests and verifies routing and flow measurement functions of the architecture.
Finite-Size Corrections to Anomalous Dimensions in N=4 SYM Theory

The scaling dimensions of large operators in = 4 supersymmetric Yang-Mills theory are dual to energies of semiclassical strings in AdS5 × S5. At one loop, the dimensions of large operators can be computed with the help of Bethe ansatz and can be directly compared to the string energies. We study finite-size corrections for Bethe states which should describe quantum corrections to energies of extended semiclassical strings.
High Performance AODV Routing Protocol for Hybrid Wireless Mesh Networks

Hybrid wireless mesh networks are multi-hop networks consisting of two types of nodes, mesh routers and mesh clients. Mesh routers are more static and less resource constrained than mobile mesh clients, and form the wireless backhaul of the network. Routing in hybrid wireless mesh networks is a challenging task as both type of nodes participate in the routing and forwarding of packets. In this paper, we present extensions to the ad-hoc on-demand distance vector (AODV) routing protocol with the aim to exploit the heterogeneity of hybrid wireless mesh networks. As demonstrated via extensive simulations, our extensions achieve a more than 100% improvement over the standard multi-radio AODV in terms of key performance metrics such as packet delivery ratio, routing overhead and latency.
The design of a real-time Warehouse Management System that integrates simulation and optimization models with RFID technology

In this paper we approach a typical warehouse problem that is common to most industrial businesses, were efficiency is given by the best arrangement of items in the warehouse (slotting) and the best picking routes selected for each order (picking). Yet, when implementing picking and slotting optimization techniques, the information used is historical data that, in most cases, would fail to work given the many factors that affect daily warehousing operations. On the other hand, simulation models have been used to make Ramírez Ríos, Diana, Ramírez Polo, Luis, Jimenez Barros, Miguel, Castro Bolaño, Lauren, Maldonado, Erik, The International Journal of Computer Science & Applications (TIJCSA) ISSN – 2278-1080, Vol. 2 No. 04 June 2013 © 2013, http://www.journalofcomputerscience. com TIJCSA All Rights Reserved 19 virtual scenarios of a warehouse and can predict, with some accuracy, what would happen if a specific decision was made, but these models also fail to work, due to the fact that the data collected is not a reliable one. Given the complexity of most warehouses, we have designed a Warehouse Management System (WMS) that functions dynamically with real-time data, through the use of RFID technology and the integration of simulation and optimization models. KeywordsOptimization, RFID, Slotting, Simulation, Warehousing, WMS
Implementing results-based management systems in Zimbabwe: Context and implications for the public sector

The article focuses on the introduction of the new concept of results-based management (RBM) in Zimbabwe’s public sector. The major tenets of RBM are explored and examined against traditional implementation-focused public management systems. The study investigated the benefits from RBM, problems being encountered and lessons from experience. Zimbabwe’s experience with a results-based management programme has also been examined against global experiences from other countries. Documentary search and field research revealed that in addition to some epochs of progress the politics of administration, forces of patron-clientelism and rampant administrative and technical incapacity have been the major setbacks to the fruition of RBM. This revelation casts doubt on whether a proper readiness assessment was conducted before the initiative’s introduction. On the contrary a review of literature indicates that although the implementation of a results-based management system is easier said than done, it still remains an indispensable tool of effective public management.
Experiments in the detection of upper limb posture through kinestetic strain sensors

Conductive elastomers are strain-sensing technology which can be embedded unobtrusively into a garment's fabric. A prototype was realized to simultaneously measure the strains at multiple points of a shirt covering the thorax and upper limb. This paper describes preliminary experiments with machine learning techniques, employed to analyse the strain measures in order to reliably reconstruct upper-limb posture. The scope of the application is to detect execution, correctness and progress of physical exercises performed as part of neurological rehabilitation therapy
Requirements, Plato's Cave, and Perceptions of Reality

Software developers build systems in response to agreed-upon requirements as if those requirements were absolutely perfect. Those of us in the requirements field know that the process of creating and documenting requirements is extremely error- prone. In fact, so error prone that we wonder why developers (a) accept them as truth, and then to make matters worse, (b) make it so difficult to change them when problems are eventually discovered. This paper draws an analogy between the process of requirements determination and Plato's allegory of the cave. Specifically, it describes requirements problems that arise when our perceptions of reality differ from actual reality.