Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Optimal LSB Substitution Using Ant Colony Optimization Algorithm

The Least Significant Bit (LSB) Substitution is a kind of information hiding method. The secret message is embedded into the last r bits of a cover image to get away from the notice of hackers. The security and stego-image quality are two main issues of the LSB substitution method. Therefore, some researchers propose an LSB substitution matrix to address these two issues. Finding an optimal LSB substitution matrix can be seen as a problem of combinatorial optimization. There are 2r! feasible solutions, hence the search space grows notably when r increases. Consequently, an efficient method to construct the matrix is necessary. In this paper, we apply the Ant Colony Optimization Algorithm to construct an optimal LSB substitution matrix. The experimental results show that ACO can find optimal LSB substitution matrix efficiently and improve the qualities of the stego-images.
A Novel Cysteine Cross-linking Method Reveals a Direct Association between Claudin-1 and Tetraspanin CD9*S

Tetraspanins serve as molecular organizers of multiprotein microdomains in cell membranes. Hence to understand functions of tetraspanin proteins, it is critical to identify laterally interacting partner proteins. Here we used a novel technical approach involving exposure and cross-linking of membrane-proximal cysteines coupled with LC-MS/MS protein identification. In this manner we identified nine potential tetraspanin CD9 partners, including claudin-1. Chemical cross-linking yielded a CD9-claudin-1 heterodimer, thus confirming direct association and adding claudin-1 to the short list of proteins that can directly associate with CD9. Interaction of CD9 (and other tetraspanins) with claudin-1 was supported by subcellular colocalization and was confirmed in multiple cell lines, although other claudins (claudin-2, -3, -4, -5, and -7) associated to a much lesser extent. Moreover claudin-1 was distributed very similarly to CD9 in sucrose gradients and, like CD9, was released from A431 and A549 cells upon cholesterol depletion. These biochemical features of claudin-1 are characteristic of tetraspanin microdomain proteins. Although claudins are major structural components of intercellular tight junctions, CD9-claudin-1 complexes did not reside in tight junctions, and depletion of key tetraspanins (CD9 and CD151) by small interfering RNA had no effect on paracellular permeability. However, tetraspanin depletion did cause a marked decrease in the stability of newly synthesized claudin-1. In conclusion, these results (a) validate a technical approach that appears to be particularly well suited for identifying protein partners directly associated with tetraspanins or with other proteins that contain membrane-proximal cysteines and (b) provide insight into how non-junctional claudins may be regulated in the context of tetraspanin-enriched microdomains.
Bounded gossip: a gossip protocol for large-scale datacenters

Gossip-based protocols are very robust and are able to distribute the load uniformly among all nodes. Furthermore, gossip-protocols circumvent the oscillatory phenomena that are known to occur with other forms of reliable multicast. As a result, they are excellent candidates to support the dissemination of information in large-scale datacenters. However, in this context, topology oblivious approaches may easily saturate the switches in the highest level of the datacenter network fabric. This paper presents and evaluates a novel gossip protocol for datacenters, named Bounded Gossip, that provides an adequate load distribution among the different layers of the switching fabric of the datacenter, avoiding being a source of network bottlenecks.
Dlx1 and Rgs5 in the Ductus Arteriosus: Vessel-Specific Genes Identified by Transcriptional Profiling of Laser-Capture Microdissected Endothelial and Smooth Muscle Cells

Closure of the ductus arteriosus (DA) is a crucial step in the transition from fetal to postnatal life. Patent DA is one of the most common cardiovascular anomalies in children with significant clinical consequences especially in premature infants. We aimed to identify genes that specify the DA in the fetus and differentiate it from the aorta. Comparative microarray analysis of laser-captured microdissected endothelial (ECs) and vascular smooth muscle cells (SMCs) from the DA and aorta of fetal rats (embryonic day 18 and 21) identified vessel-specific transcriptional profiles. We found a strong age-dependency of gene expression. Among the genes that were upregulated in the DA the regulator of the G-protein coupled receptor 5 (Rgs5) and the transcription factor distal-less homeobox 1 (Dlx1) exhibited the highest and most significant level of differential expression. The aorta showed a significant preferential expression of the Purkinje cell protein 4 (Pcp4) gene. The results of the microarray analysis were validated by real-time quantitative PCR and immunohistochemistry. Our study confirms vessel-specific transcriptional profiles in ECs and SMCs of rat DA and aorta. Rgs5 and Dlx1 represent novel molecular targets for the regulation of DA maturation and closure.
UML behavioral model based test case generation: a survey

In software engineering, system modeling is the process of formulating a representation of a real system in an abstract way to understand its behavior. Software testing encourages reusing these models for testing purpose. This expedites the process of test case generation. UML structural and behavioral specification diagrams have been used by testing researchers for generation of test scenarios and test data. The aim of this survey is to improve the understanding of UML based testing techniques. We have focused on test case generation from the behavioral specification diagrams, namely sequence, state chart and activity diagrams. We classify the various research approaches that are based on formal specifications, graph theoretic, heuristic testing, and direct UML specification processing. We discuss the issues of test coverage associated with these approaches.
Sparse keypoint models for 6D object pose estimation

In this paper, we present an approach to generate sparse object models for keypoint-based 6D object pose estimation. Keypoint-based object models usually consist of thousands of keypoints. Our approach generates sparse models by identifying and removing keypoints that are not relevant to the object localization. It applies data association to detect duplicate keypoints and applies statistical analysis to identify keypoints that have not been detected reliably during model generation. Our approach furthermore ensures that keypoints are well distributed across the volume of the object model. We evaluated our approach using a SIFT-based 6D object localization system on the basis of real world datasets. In our experiments, we achieved a reduction of the model sizes to approximately 1% of the original model size without a substantial loss of localization performance.
COMPUTATIONAL MODELING OF SIMULTANEOUSLY RECORDED SCALP AND DEPTH EEG SIGNALS: INSIGHTS INTO THE INTERPRETATION OF INTERICTAL EPILEPTIC ACTIVITY

In epileptic patients candidate to surgery, the interpretation of electrophysiological signals recorded non-invasively (scalp EEG) and invasively (depth EEG) is a difficult but central question. Indeed, the localization of the epileptogenic zone, the determination of its organization and the definition of subsequent therapeutic strategy are still largely based on the analysis of electrophysiological data. This issue is addressed in the present work through a realistic modeling of both scalp and depth EEG signals. The model is based on an anatomically and physiologically relevant description of the neuronal sources of brain electrical activity that combines a distributed dipole source model with a model of coupled neuronal populations. EEG signals are then simulated by solving the so-called forward problem in the head volume conductor, simultaneously on scalp and depth electrodes. The model allows for the study of the influence, on simulated EEG signals, of source-related parameters (spatial extent, synchronization) leading to the generation of transient epileptic activity (interictal spikes). More generally, this modeling approach helps in the understanding of the relationship between the properties of signals collected by electrodes (scalp and depth) and the underlying spatio-temporal organization of the neuronal sources.
Modeling the Potential for Vaccination to Diminish the Burden of Invasive Non-typhoidal Salmonella Disease in Young Children in Mali, West Africa

Background In sub-Saharan Africa, systematic surveillance of young children with suspected invasive bacterial disease (e.g., septicemia, meningitis) has revealed non-typhoidal Salmonella (NTS) to be a major pathogen exhibiting high case fatality (~20%). Where infant vaccination against Haemophilus influenzae type b (Hib) and Streptococcus pneumoniae has been introduced to prevent invasive disease caused by these pathogens, as in Bamako, Mali, their burden has decreased markedly. In parallel, NTS has become the predominant invasive bacterial pathogen in children aged <5 years. While NTS is believed to be acquired orally via contaminated food/water, epidemiologic studies have failed to identify the reservoir of infection or vehicles of transmission. This has precluded targeting food chain interventions to diminish disease transmission but conversely has fostered the development of vaccines to prevent invasive NTS (iNTS) disease. We developed a mathematical model to estimate the potential impact of NTS vaccination programs in Bamako. Methodology/Principal Findings A Markov chain transmission model was developed utilizing age-specific Bamako demographic data and hospital surveillance data for iNTS disease in children aged <5 years and assuming vaccine coverage and efficacy similar to the existing, successfully implemented, Hib vaccine. Annual iNTS hospitalizations and deaths in children <5 years, with and without a Salmonella Enteritidis/Salmonella Typhimurium vaccine, were the model’s outcomes of interest. Per the model, high coverage/high efficacy iNTS vaccination programs would drastically diminish iNTS disease except among infants age <8 weeks. Conclusions/Significance The public health impact of NTS vaccination shifts as disease burden, vaccine coverage, and serovar distribution vary. Our model shows that implementing an iNTS vaccine through an analogous strategy to the Hib vaccination program in Bamako would markedly reduce cases and deaths due to iNTS among the pediatric population. The model can be adjusted for use elsewhere in Africa where NTS epidemiologic patterns, serovar prevalence, and immunization schedules differ from Bamako.
Hardness of Approximation for Sparse Optimization with L 0 Norm

In this paper, we consider sparse optimization problems with L0 norm penalty or constraint. We prove that it is strongly NP-hard to find an approximate optimal solution within certain error bound, unless P = NP. This provides a lower bound for the approximation error of any deterministic polynomialtime algorithm. Applying the complexity result to sparse linear regression reveals a gap between computational accuracy and statistical accuracy: It is intractable to approximate the estimator within constant factors of its statistical error. We also show that differentiating between the best k-sparse solution and the best (k+ 1)-sparse solution is computationally hard. It suggests that tuning the sparsity level is hard.
konomische Konsequenzen der Migration fr den heimischen Arbeitsmarkt

The purpose of this study was an evaluation of the labor market consequences of immigration. The European countries had substantial experiences with labor migration in recent years. The various theoretical channels how natives might be affected by foreign labor are discussed, especially the issue whether natives and foreigners are complements or substitutes. Most empirical studies for the US confirm that the employment and wage processes of natives are hardly affected. However, recent investigations for Germany qualify these findings.
Relief R-CNN: Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment

R-CNN style models are the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification for objects. The proposal generation phase in this paradigm is usually time consuming and not convenient to be deployed on hardwares with limited computational ability. This article shows that the high-level patterns of feature value in deep convolutional feature maps contain plenty of useful spatial information, and propose a simple manually designed approach that can extract the information for fast region proposal generation. The proposed method, dubbed Relief R-CNN($R^2$-CNN), is a new deep learning approach for object detection. By extracting positions of objects from high-level convolutional patterns, $R^2$-CNN generates region proposals and performs deep classification simultaneously based on the same forwarding CNN features, unifies the formerly separated object detection process and speeds up the whole pipeline without additional training. Empirical studies show that $R^2$-CNN could achieve the best trade-off between speed and detection performance among all the comparison algorithms.
Social media and Iran's post-election crisis

In this research-in-progress paper, the role of social media during the two months of the Iranian post-election crisis in Summer 2009 has been studied. In search of emergent social phenomena, particular emphasis is given to online participation and collaboration throughout social network sites. This study demonstrates the extent to which such media can gain prominence and challenge traditional practices as well as challenging the next level of research and development of social media during crisis situations.
REGRESSION PERFORMANCE OF GROUP LASSO FOR ARBITRARY DESIGN MATRICES

In many linear regression problems, explanatory variables are activated in groups or clusters; group lasso has been proposed for regression in such cases. This paper studies the nonasymptotic regression performance of group lasso using `1/`2 regularization for arbitrary (random or deterministic) design matrices. In particular, the paper establishes under a statistical prior on the set of nonzero coefficients that the `1/`2 group lasso has a near-optimal regression error for all but a vanishingly small set of models. The analysis in the paper relies on three easily computable metrics of the design matrix – coherence, block coherence, and spectral norm. Remarkably, under certain conditions on these metrics, the `1/`2 group lasso can perform near-ideal regression even if the model order scales almost linearly with the number of rows of the design matrix. This is in stark contrast with prior work on the regression performance of the `1/`2 group lasso that only provides linear scaling of the model order for the case of random design matrices. Keywords— Group sparsity, linear regression, group lasso, coherence, block coherence
Spatial-Temporal Based Traffic Speed Imputation for GPS Probe Vehicles

Due to the growth of vehicular network and big data analytics, missing data of traffic detector devices become a serious problem in analytics and applications of intelligent transportation systems. The purpose of data imputation is to complete the shortage of traffic data. In this paper, a spatial-temporal based data imputation for GPS probe vehicle in intelligent transportation systems is proposed. In the proposed system, GPS data with speed of vehicles are located into the map within corresponding road segments by GPS coordinates using R+-tree and Dijkstra's algorithm. Then, spatial features are extracted from the current road segment and its two neighboring segments' speeds, and temporal features are extracted from the current time sector, weekday, and speeds of the current road segment in 5 and 10 minutes ago, respectively. After that, each model of road segment is trained by support vector regression with spatial-temporal features for data imputation. Experimental results show that the proposed scheme is better than Gaussian processing with time series feature at different missing rates.
A Note on the Discovery of Absorption Features in 1E 1207.4-5209

Sanwal et al. [Astrophys. J. 574 (2002) L61] supposed that it is very difficult to interpret the absorption features in terms of cyclotron lines. However, we would like to address here that the possibility of the absorption being cyclotron resonance cannot be ruled out. We propose that the isolate neutron star, 1E 1207.4-5209 in the centre of supernova remnant PKS 1209-51/52, has a debris disc and is in a propeller phase, with an accretion rate ~6×10-11M⊙/year. In this scenario, 1E 1207.4-5209 could also be a bare strange star.
A Multi-Pass Multi-Stage Multi-GPU Collision Detection Algorithm

We present the work in progress of a collision detection (CD) algorithm on a multi-threaded environment and based on two graphics processor units (GPUs); the first GPU is used as a normal graphics processor, and the second GPU is doubled as a ‘collision detection’ coprocessor. Our approach makes use of the stream parallel engine provided by modern GPUs together with their native architecture for handling vector operations; thus we aim to perform fast and reliable collision detection. Our initial work involves multi-threading and incorporates a cheap preprocessing stage for storing essential scene data in a format suitable for GPUs (i.e. as textures). It also involves multi-pass rendering in multiple stages. The first stage of collision detection is based on a simple boundary volume collision query. If the result is positive, a second stage is then executed, involving objects pair-wise computations on a per-vertex basis. The approach is being implemented on a single Processor NVIDIA Single-Link Interface (SLI) [1] System with two NVIDIA GeForce 6600GT graphics cards.
Privacy-Centric Access Control for Distributed Heterogeneous Medical Information Systems

In many jurisdictions, patients are being increasingly empowered to play a critical role in defining how their medical information can be collected, used and shared across various healthcare data custodians. This patient-centric focus on information custody and management, along with the highly distributed nature of medical information, introduces new access control challenges related to privacy and security of medical information. As a result, when exchanging medical information across systems under different administrative domains, traditional access control models are not effective to enforce patient privacy preferences. To address this challenge, we propose an access control scheme that is patient-centric and offers a consent-based access control solution usable across heterogeneous medical information systems. Our model utilizes a logic-based approach to make inferences about access control decisions, and uses ontology-based knowledge representation to ensure that privacy preferences are correctly understood and applied. All system-level access control decisions can be automated and independently verified for validity and correctness. Our proposed solution offers a flexible and robust model that is most suited for the demanding access control scenarios present in patient care.
Implementing a t-way test generation strategy using bees algorithm

In order to ensure software performance as well as software quality, various testing techniques have been used to detect faults as early and as many as possible during the development phase. Over the last decade, the size and complexity of software developed have increased tremendously. Highly customizable software allow users to configure the software to the users’ needs, however, if not tested adequately, these software are prone to interaction faults. This paper discusses our experiences on implementation of Bees algorithm for generating test cases to detect t-way interaction faults (where t signifies the interaction strength).
Panhinda - Offline Character Recognition System for Handwritten Articles

This paper presents an innovative technique to recognize Handwritten Articles. Proposed system is called "Panhinda". The target user group for this application would be the people who are involved with a lot of paper work on a daily basis. The proposed Character Recognition system was implemented with the capability of extracting the content of an image where the mentioned content is a hand written set of words or characters. The conversion process runs as a background process without any involvement of the user. Once the conversion is completed, User gets the capability of editing the converted text as he prefers with the aid of the Panhinda editor. This document describes the techniques for enhancing the quality of the image, character segmentation, character recognition and digital dictionaries. Noise removal, angle effects and lighting conditions are done at the pre-processing phase. After getting a quality binarized image, character segmentation will be done using Horizontal and Vertical Projection Profile method. The Support Vector Machine technique will be used to recognize the characters. Digital Dictionary will be used to capture the conflicts of the output. Error correction will be done by using a combined model of noisy channel model and natural language model. By walking through above mentioned processes handwritten article image will be converted into an editable text file. Experimenting with a set of 200 sample images, scanned through the Scanner, we have achieved a maximum recognition accuracy of 99.5% with manual error correction. Compared to existing commercial OCR systems, present recognition accuracy is worth contributing. Moreover, the developed technique is computationally efficient and consumes low memory.
Aspiration Noise in Speech / Singing Voice

It is hard to define aspiration noise phenomenally. Although it occurs as a “part of a voiced sound such as vowels” (Kob 2002), a distinction is made as a contrary to friction that happens on the vocal path and leads to fricative consonants (Jackson 2000). The characteristic of aspiration noise, as denoted by Jackson (2000), is broadband, with an hlike sound. It is, being more physically revealed, referred to as “turbulence noise at the glottis” in a range of publications.
An Inflated Ensemble Filter for Ocean Data Assimilation with a Biased Coupled GCM

Abstract A “biased twin” experiment using two coupled general circulation models (CGCMs) that are biased with respect to each other is used to study the impact of deep ocean bias on ensemble ocean data assimilation. The “observations” drawn from one CGCM based on the Argo network are assimilated into the other. Traditional ensemble filtering can successfully recover the upper-ocean temperature and salinity of the target model but it usually fails to converge in the deep ocean where the model bias is large compared to the ocean’s intrinsic variability. The inconsistency between the well-constrained upper ocean and poorly constrained deep ocean generates spurious assimilation currents. An adaptively inflated ensemble filter is designed to enhance the consistency of upper- and deep-ocean adjustments, based on “climatological” standard deviations being adaptively updated by observations. The new algorithm reduces deep-ocean errors greatly, in particular, reducing current errors up to 70% and vertical motion e.