Miracle-dz/UniFAR-FTUs huggingface dataset:
Split-Step-Fourier-Method in Modeling Wavelength-Division-Multiplexed Links

We review different variants of split-step-Fourier-method (SSFM) used for simulation of wavelength-division- multiplexed links. We analyse single-band as well as multi-band methods. Local-error-method (LEM) is especially useful in single-band simulations, because it automatically adjust simulation step for required accuracy. For large number of WDM channels all single-band methods, including LEM, show prohibitively long simulation time. In this case multi-band methods are being used. We evaluate different multi-band methods and propose application of LEM method to cross-phase modulation (XPM) simulation in place of fixed step, which improves simulation speed and accuracy.
Source Localisation with a Bernoulli Particle-Filter-Based Bearings-Only Tracking Algorithm

Locating the source of a chemical or acoustic emitter in an unknown environment with noisecorrupted detector signals is a challenging task undertaken regularly in emergency-response operations. Such missions can endanger personnel, thus the use of autonomous systems would be highly advantageous. In this research, an approach for autonomous bearings-only tracking (BOT) is trialled in a proof-of-concept experiment relying on a bright light source and a sensor constructed from photodiodes and carried atop a radio-controlled ground vehicle. The goal is to test the algorithm’s performance with real-world, noise-corrupted data. A Bernoulli particle-filter algorithm, designed to tolerate cluttered signals, is used to estimate the location of the light source from low-resolution measurements of its bearing. Data is collected with a variety of navigational patterns in an environment that occasionally produces false detections. The algorithm yields accurate estimates of the location of the light source (on average, 22 cm from a source with a 16-cm horizontal aperture); however, in some cases, false detections result in poor estimation of the source position and large uncertainties. Future research will aim to address the limitations of the current trial and to implement the algorithm on an autonomous platform.
Blind Restoration of Fluorescein Angiography Images

In this paper, a new technique is presented to enhance the blurred images obtained from Fluoresce in Angiography (FA) of the retina. One of the main steps in inspecting the eye (especially the deeper image of retina) is to look into the eye using a slit-lamp apparatus that shines a monochromatic light on to the retinal surface and captures the reflection in the camera as the retinal image. When further probing is required, such imaging is preceded by injecting a specialized dye in the eye blood vessels. This dye shines out more prominently in the imaging system and reveals the temporal as well as special behavior of the blood vessels, which, in turn, is useful in the diagnosis process. While most of the cases, the image produced is quite clean and easily used by the ophthalmologists, there are still many cases in which these images come out to be very blurred due to the disease in the eye such as cataract etc… in such cases, having an enhanced image can enable the doctors to start the appropriate treatment for the underlying disease. The proposed technique utilizes the Blind Deconvolution approach using Maximum Likelihood Estimation approach. Further post-processing steps have been proposed as well to locate the Macula in the image which is the zero-center of the image formed on the retina. The post-processing steps include thresholding, Region Growing, and morphological operations.
Efficient Top-K Retrieval in Online Social Tagging Networks

We consider in this paper top-k query answering in social tagging systems, also known as folksonomies. This problem requires a significant departure from existing, socially agnostic techniques. In a network-aware context, one can (and should) exploit the social links, which can indicate how users relate to the seeker and how much weight their tagging actions should have in the result build-up. We propose an algorithm that has the potential to scale to current applications. While the problem has already been considered in previous literature, this was done either under strong simplifying assumptions or under choices that cannot scale to even moderate-size real world applications. We first consider a key aspect of the problem, which is accessing the closest or most relevant users for a given seeker. We describe how this can be done on the fly (without any pre-computations) for several possible choices - arguably the most natural ones - of proximity computation in a user network. Based on this, our top-k algorithm is sound and complete, while addressing the scalability issues of the existing ones. Importantly, our technique is instance optimal in the case when the search relies exclusively on the social weight of tagging actions. To further reduce response times, we then consider directions for efficiency by approximation. Extensive experiments on real world data show that our techniques can drastically improve the response time, without sacrificing precision.
The interface of politeness strategies and power relations in disagreements among Egyptian students

Disagreement is generally done in opposition to a speaker's claim, in an educational context it can be defined as a student's verbal or non-verbal opposition to classmates' or the teacher's stance that emerge through different instructional and non-instructional situations. The purpose of the study is to qualitatively examine Egyptian undergraduates' disagreements with participants of different power relations in EFL classrooms from a politeness theory perspective. The study introduces Egyptian EFL classrooms as a new context for studying the interface of power and politeness in disagreements in general and for exploring the impact of other variables such as context, social distance and type of interactional activities on the realization of disagreements. Data were collected through videotaping two classes of business-English, with a total of 18 hours of observation. In addition, interviews were conducted with a sub-set of the participants to gather in-depth information about the students’ pragmatic choices and a questionnaire evaluating social distance between peers was administered to all participants. In the 18 hours of data collected, 34 students expressed 90 turns of disagreement; 35 of these were directed to the teacher, while 55 were to peers. Spontaneous disagreements were coded and categorized according to the Brown and Levinson (1987) politeness theory using Rees-Miller’s (2000) taxonomy, adapted here to include strategies from Muntigl and Turnbull's (1998) taxonomy. Analysis of the data showed that although students employed various positive and negative politeness strategies to soften disagreements when
GMFR-CNN: An Integration of Gapped Motif Feature Representation and Deep Learning Approach for Enhancer Prediction

Unravelling gene expression has become a critical procedure in bioinformatics world today and required continuous efforts to form a complete picture of enhancers. Enhancers are explicit patterns of gene expression that bound by activators to stimulate transcription. It could reside in upstream or downstream thousands of base pairs away without any fixed position. Therefore, the identification task of enhancers is extremely challenging. The inclusion of gaps in motif identification improved the overall accuracy and sensitivity, however, this feature is not fully utilised in deep learning method yet. Deep learning, is a powerful machine learning technique that has been actively used in image recognition and this technique has begun to shed light in bioinformatics. The expressiveness of deep learning enables higher feature learning from lower level ones. As a result, an integration of gapped motif feature representation (GMFR) and deep learning approach called deep convolutional neural networks (CNNs) is introduced to improve the predictive power of enhancer prediction. We called this method as GMFR-CNN. Comparative studies indicate that GMFR-CNN outperforms the other deep learning and gapped k-mer SVM tools with average 98% prediction accuracy. Breakthrough in deep learning technique certainly improves the performance in the near future.
Detecting Changes over Time in a Knowledge Sharing Community

There is an establishing trend towards the socialization of the web. Virtual communities are becoming very popular web spaces for collaboration and knowledge sharing. However, studies have shown that virtual communities may often be ineffective and may not sustain. We propose a novel approach for community-tailored support which is aimed at facilitating processes important for the community’s effectiveness and sustainability. The paper presents algorithms for detecting evolution patterns of community knowledge sharing behavior. The algorithms are applied to a model of an existing closely-knit community, and used to identify when and what intelligent interventions may be needed to support the functioning of the community as an entity.
Static binary rewriting without supplemental information: Overcoming the tradeoff between coverage and correctness

Binary rewriting is the process of transforming executables by maintaining the original binary's functionality, while improving it in one or more metrics, such as energy use, memory use, security, or reliability. Although several technologies for rewriting binaries exist, static rewriting allows for arbitrarily complex transformations to be performed. Other technologies, such as dynamic or minimally-invasive rewriting, are limited in their transformation ability. We have designed the first static binary rewriter that guarantees 100% code coverage without the need for relocation or symbolic information. A key challenge in static rewriting is content classification (i.e. deciding what portion of the code segment is code versus data). Our contributions are (i) handling portions of the code segment with uncertain classification by using speculative disassembly in case it was code, and retaining the original binary in case it was data; (ii) drastically limiting the number of possible speculative sequences using a new technique called binary characterization; and (iii) avoiding the need for relocation or symbolic information by using call translation at usage points of code pointers (i.e. indirect control transfers), rather than changing addresses at address creation points. Extensive evaluation using stripped binaries for the entire SPEC 2006 benchmark suite (with over 1.9 million lines of code) demonstrates the robustness of the scheme.
The prevalence of Borrelia burgdorferi (Spirochaetales: Spirochaetaceae) and the Agent of Human Granulocytic Ehrlichiosis (Rickettsiaceae: Ehrlichieae) in Ixodes scapularis (Acari:Ixodidae) Collected During 1998 and 1999 from Minnesota

Abstract We tested 103 adult Ixodes scapularis Say from 12 counties in Minnesota for the presence of Borrelia burgdorferi and the causative agent of human granulocytic ehrlichiosis (HGE), using polymerase chain reaction (PCR). A total of 17 ticks (16.5%) was positive for B. burgdorferi using nested PCR for the flagellin gene, or both PCR for the ospA gene and nested PCR for the flagellin gene. A total of four ticks (3.8%) was positive for the agent of HGE using nested PCR for 16S rDNA. Counties in Minnesota with established and recently reported populations of I. scapularis both had ticks infected with B. burgdorferi. The agent of HGE was only detected in counties with established I. scapularis populations.
A Proposed Block-Coding Technique of an Image based on Fractal Compression

Now a day the fractal image compression technique models a natural image using a contractive mapping called fractal mapping in the image space. Due to reducing the search complexity of matching between range block and domain block in fractal image compression is one of the most active research areas lately. There are two main characteristics of this approach are (a) It relies on the assumption that image redundancy can be efficiently captured and exploited through piecewise serf-transformability on a block-wise basis, and (b) It approximates an original image by a fractal image, obtained from a finite number of iterations of an image transformation called a fractal code. This paper proposed to this approach as Fractal Block Coding. For such an application, the general problem statement is the following. For any given original discrete image specified by an array of pixels, how can a computer construct a fractal image, the coded image-which is both visually close to the original one, and has a digital representation which requires fewer bits than the original image? The proposed coding scheme carried out an approach to image coding based on a fractal theory contractive transformations defined piecewise. In experimental results show that compared with Jacquin coding scheme and our proposed coding scheme achieves an average of 89% reduction in encoding time and improves the efficiency of search. Simultaneously the compression ratio and quality of decoded images are guaranteed to the same as Jacquin coding scheme for the same images. General Terms Digital image processing, Pattern Recognition
Computational information geometry: theory and practice

This paper lays the foundations for a unified framework for numerically and computationally applying methods drawn from a range of currently distinct geometrical approaches to statistical modelling. In so doing, it extends information geometry from a manifold based approach to one where the simplex is the fundamental geometrical object, thereby allowing applications to models which do not have a fixed dimension or support. Finally, it starts to build a computational framework which will act as a proxy for the 'space of all distributions' that can be used, in particular, to investigate model selection and model uncertainty. A varied set of substantive running examples is used to illustrate theoretical and practical aspects of the discussion. Further developments are briefly indicated.
Task priority approach to the coordinated control of a team of flying vehicles in the presence of obstacles

This study describes the application of the task priority null-space behavioural technique to the coordinated control of a team of flying vehicles with hovering capabilities, such as helicopters or quadrotors, in the presence of obstacles and no-fly zones. Once a flight mission is assigned to the team in terms of a target region to reach, each flying vehicle is required to accomplish four tasks with assigned priorities. Formation flight and collisions avoidance with other vehicles and unknown or moving obstacles tasks are formulated via analytical expressions as required by the classical null-space behavioural approach. Move to target and a priori known obstacle avoidance behaviours are obtained by solving a partial differential equation problem within the flight domain. The effectiveness of the proposed technique is discussed with regards to two-dimensional and three-dimensional numerical examples.
Automated optic nerve head image fusion of nonhuman primate eyes using heuristic optimization algorithm

Multi-sensor biomedical image registration and fusion usually require intensive computational effort. This article presented a novel automated approach of the multi-sensor retinal optic nerve head image registration and fusion using heuristic optimization algorithm. The reference and the to-be-registered images are from two different modalities, i.e. angiogram grayscale images and fundus color images. The optic nerve head vasculature is extracted using Canny edge detector. Control points are detected at the vessel bifurcations using adaptive exploratory algorithm. mutual-pixel-count (MPC) maximization based heuristic optimization adjusts the control points at the sub-pixel level. The iteration stops either when MPC reaches the maximum value, or when the maximum allowable loop count is reached. A refinement of the parameter set is obtained at the end of each loop, and finally an optimal fused image is generated at the end of the iteration. Comparative evaluation is performed with the genetic algorithm. The results show the advantages of the presented method in terms of novelty, efficiency and accuracy.
Connectivity Reestablishment in Self-Organizing Sensor Networks with Dumb Nodes

In this work, we propose a scheme, named CoRAD, for the reestablishment of lost connectivity using sensor nodes with adjustable communication range in stationary wireless sensor networks (WSNs), when “dumb” behavior occurs some of the nodes. Due to the occurrence of such behavior, there may be temporary loss of connectivity between among the nodes. Such a phenomenon is different from the commonly known node isolation problem in stationary WSNs. The mere activation of intermediate sleep nodes cannot guarantee reestablishment of connectivity, because there may not exist neighbor nodes of the isolated nodes. On the contrary, the increase in communication range of a single sensor node may make it die quickly. Including this, a sensor node has maximum limit of increase in communication range that may not be sufficient to reestablish connectivity. Therefore, considering all these factors for self-organization of the network and isolated node re-connection, we propose a price-based scheme, which addresses the issue by activating intermediate sleep nodes or by adjusting the communication range of some of the other nodes in the network. The scheme also deactivates the additional activated nodes and reduces the increased communication range when the dumb nodes resume their normal behavior, upon the return of favorable environmental conditions. To implement the proposed scheme, CoRAD it is required to construct the network using GPS-enabled adjustable communication range sensor nodes. Through simulation we compare our proposed scheme with the existing topology management schemes -- LETC and A1 -- in the same scenario by considering the number of activated nodes, message overhead, and energy consumption. We find that the proposed scheme shows improved performance compared to the existing topology management schemes.
A Simple Computational Model for Texture Based Image Retrieval with Orthogonal Polynomials

—The exponential growth of digital image data has created a great demand for effective and efficient scheme and tools for browsing, indexing and retrieving images from a collection of large image databases. To address such a demand, this paper proposes a new content based image retrieval technique with orthogonal polynomials model. The proposed model extracts texture features that represent the dominant directions, gray level variations and frequency spectrum of the image under analysis and the resultant texture feature vector becomes rotation and scale invariant. A new distance measure called Deansat is proposed as a similarity measure that uses the proposed feature vector for efficient image retrieval. The efficiency of the proposed retrieval technique is experimented with the standard Brodatz, USC-SIPI databases and is compared with Discrete Cosine Transform (DCT), Tree Structured Wavelet Transform (TWT) and Gabor filter based retrieval schemes. The experimental results reveal that the proposed method outperforms well.
CIREC : Cluster Correlogram Image Retrieval and Categorization using MPEG-7 Descriptors

Content-based image retrieval is generally about understanding of information in the images concerned. The more the system is able to understand the content of images the more effective it will be in retrieving desired images. In this paper, we have developed a method that combines a clustering technique with the color correlation histogram. According to this method, an image is divided into several blocks using a fixed partitioning scheme. Then, selected lower-level MPEG-7 features are used to represent the partitions and a clustering technique is applied to group similar patterns into a cluster. The cluster indices will then be used to construct a corellogram which is not based on the lower-level primitives, but on regions consisting of a distribution of primitives. The result shows that the proposed method significantly outperforms other methods on image retrieval and categorization
A Novel Peptide from Soybean Protein Isolate Significantly Enhances Resistance of the Organism under Oxidative Stress

Recent studies have indicated that protein hydrolysates have broad biological effects. In the current study we describe a novel antioxidative peptide, FDPAL, from soybean protein isolate (SPI). The aim of this study was to purify and characterize an antioxidative peptide from SPI and determine its antioxidative mechanism. LC–MS/MS was used to isolate and identify the peptide from SPI. The sequence of the peptide was determined to be Phe-Asp-Pro-Ala-Leu (FDPAL, 561 Da). FDPAL can cause significant enhancement of resistance to oxidative stress both in cells as well as simple organisms. In Caenorhabditis elegans (C. elegans), FDPAL can up-regulate the expression of certain genes associated with resistance. The antioxidant activity of this peptide can be attributed to the presence of a specific amino acid sequence. Results from our work suggest that FDPAL can facilitate potential applications of proteins carrying this sequence in the nutraceutical, bioactive material and clinical medicine areas, as well as in cosmetics and health care products.
Security analysis on a conference scheme for mobile communications

The conference key distribution scheme (CKDS) enables three or more parties to derive a common conference key to protect the conversation content in their conference. Designing a conference key distribution scheme for mobile communications is a difficult task because wireless networks are more susceptible to attacks and mobile devices usually obtain low power and limited computing capability. In this paper we study a conference scheme for mobile communications and find that the scheme is insecure against the replay attack. With our replay attack, an attacker with a compromised conference key can cause the conferees to reuse the compromised conference key, which in turn completely reveals subsequent conversation content
Image denoising using local tangent space alignment

We propose a novel image denoising approach, which is based on exploring an underlying (nonlinear) lowdimensional manifold. Using local tangent space alignment (LTSA), we 'learn' such a manifold, which approximates the image content effectively. The denoising is performed by minimizing a newly defined objective function, which is a sum of two terms: (a) the difference between the noisy image and the denoised image, (b) the distance from the image patch to the manifold. We extend the LTSA method from manifold learning to denoising. We introduce the local dimension concept that leads to adaptivity to different kind of image patches, e.g. flat patches having lower dimension. We also plug in a basic denoising stage to estimate the local coordinate more accurately. It is found that the proposed method is competitive: its performance surpasses the K-SVD denoising method.