Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraint-based Evolutionary Testing of Autonomous Distributed Systems

Distributed software systems are characterized by increasing autonomy. They often have the capability to sense the environment and react to it, discover the presence of other systems and take advantage of their services, adapt and re-configure themselves in accordance with the internal as well as the global state. Testing this kind of systems is challenging, and systematic and automated approaches are still missing. We propose a novel evolutionary testing framework for autonomous distributed systems. In this framework, test cases are continuously generated and executed. Our current implementation of the framework provides two techniques for the automated, continuous generation of test cases: (1) random; (2) evolutionary-mutation. Preliminary experimental results, obtained on a case study, are encouraging and indicate that evolutionary testing can complement effectively the manual one.
Autonomous Ontology Population from DBpedia based on Context Sensitive Entity Recognition

Aim of our research is to achieve user’s trust in our designed Semantic News Recommender System (SNRS). The work enriches the news items with automatic semantic annotations by adding additional layer in SNRS which will help in achieving user satisfaction by correctly inferring predictions of user interests in news domain. DBpedia has been used as an external source of information to populate the properties of entities in ontology. As DBpedia sometime gives more than one link for an entity, it becomes a challenge for automated system to decide that which link refers to correct information about the entity. Category of news items assists in identifying correct context of the entity. News has been classified into different categories based on International Press Telecommunication Council (IPTC) standards of the news industry. The SNRS will extract information about the identified entity from corresponding link. In this paper we have identified named entities in the news items using Named Entity Recognition (NER) tool, and then identified the correct context of the entity using news category based on semantic classification of news items. This has resulted in correct annotations of entities in ontology. Before incorporating NER tool with SNRS portal, we have first analyzed the result of various popular NER tools and come to know that different tools gives different results for different domains. We have selected the tool which has proven to be best for our domain. We have analyzed the results in terms of precision, recall and f-measure. A layer has also been added to handle the limitations which we have noticed in the output of the tool used. The results undoubtedly give a boost to SNRS in terms of user satisfaction by inferring semantically related other concepts and entities which could not have been identified directly through user behavior analysis. This will help in handling the issue of over specialization, sparsity and diversity with improvement in precision in personalization of news recommendations. Judgment of correct context of the entity in news items is prerequisite to achieve our goal.
Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining

It is well known that taking the Web user profiles into account can enhance the effectiveness of Web mining systems. However, due to the dynamic and complex nature of Web users, automatically acquiring worthwhile user profiles was found to be very challenging. Ontology-based user profile can possess more accurate user information. This research emphasizes on acquiring search intentions information. This paper presents a new approach of developing user profile for Web searching. The model considers the user's search intentions by the process of PTM (Pattern-Taxonomy Model). Initial experiments show that the user profile based on search intention is more useful than the generic PTM user profile. Developing user profile that contains user search intentions is essential for effective Web search and retrieval
Simplifying the Recovery Model of User-Level Failure Mitigation

As resilience research in high-performance computing has matured, so too have the tools, libraries, and languages that result from it. The Message Passing Interface (MPI) Forum is considering the addition of fault tolerance to a future version of the MPI standard, and a new chapter called User-Level Failure Mitigation (ULFM) has been proposed to fill this need. However, as ULFM usage has become more widespread, many potential users are concerned about its complexity and the need to rewrite existing codes. In this paper, we present a usage model that is similar to the usage already common in existing codes but that does not require the user to restart the application (thereby incurring the costs of re-entering the batch queue, startup costs, etc.). We use a new implementation of ULFM in MPICH, a popular open source MPI implementation, and demonstrate the ULFM usage using the Monte Carlo Communication Kernel, a proxy-app developed by the Center for Exascale Simulation of Advanced Reactors. Results show that the approach used incurs a level of intrusiveness into the code similar to that of existing checkpoint/restart models, but with less overhead.
ZNF395 Is an Activator of a Subset of IFN-Stimulated Genes

Activation of the interferon (IFN) pathway in response to infection with pathogens results in the induction of IFN-stimulated genes (ISGs) including proinflammatory cytokines, which mount the proper antiviral immune response. However, aberrant expression of these genes is pathogenic to the host. In addition to IFN-induced transcription factors non-IFN-regulated factors contribute to the transcriptional control of ISGs. Here, we show by genome wide expression analysis, siRNA-mediated suppression and Doxycycline-induced overexpression that the cellular transcription factor ZNF395 activates a subset of ISGs including the chemokines CXCL10 and CXCL11 in keratinocytes. We found that ZNF395 acts independently of IFN but enhances the IFN-induced expression of CXCL10 and CXCL11. Luciferase reporter assays revealed a requirement of intact NFκB-binding sites for ZNF395 to stimulate the CXCL10 promoter. The transcriptional activation of CXCL10 and CXCL11 by ZNF395 was abolished after inhibition of IKK by BMS-345541, which increased the stability of ZNF395. ZNF395 encodes at least two motifs that mediate the enhanced degradation of ZNF395 in response to IKK activation. Thus, IKK is required for ZNF395-mediated activation of transcription and enhances its turn-over to keep the activity of ZNF395 low. Our results support a previously unrecognized role of ZNF395 in the innate immune response and inflammation.
Nanoporous alumina as templates for multifunctional applications

Due to its manufacturing and size tailoring ease, porous anodic alumina (PAA) templates are an elegant physical-chemical nanopatterning approach and an emergent alternative to more sophisticated and expensive methods currently used in nanofabrication. In this review, we will describe the ground work on the fabrication methods of PAA membranes and PAA-based nanostructures. We will present the specificities of the electrochemical growth processes of multifunctional nanomaterials with diversified shapes (e.g., nanowires and nanotubes), and the fabrication techniques used to grow ordered nanohole arrays. We will then focus on the fabrication, properties and applications of magnetic nanostructures grown on PAA and illustrate their dependence on internal (diameter, interpore distance, length, composition) and external (temperature and applied magnetic field intensity and direction) parameters. Finally, the most outstanding experimental findings on PAA-grown nanostructures and their trends for technological appl.
Under Consideration for Publication in Theory and Practice of Logic Programming Asperix, a First Order Forward Chaining Approach for Answer Set Computing *

The natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a combinatorial problem is to elaborate a first order logic program with default negation. In a preliminary step this program with variables is translated in an equivalent propositional one by a first tool: the grounder. Then, the proposi-tional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (stable models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation. In this article, the project ASPeRiX is presented as a first order forward chaining approach for Answer Set Computing. This project was amongst the first to introduce an approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. The methodology applies a forward chaining of first order rules that are grounded on the fly by means of previously produced atoms. Theoretical foundations of the approach are presented, the main algorithms of the ASP solver ASPeRiX are detailed and some experiments and comparisons with existing systems are provided.
Ultrashort Laser Pulse Phenomena: Fundamentals, Techniques, and Applications on a Femtosecond Time Scale

Fundamentals. Femtosecond Optics. Light-Matter Interaction. Coherent Phenomena. Ultrashort Sources. Femtosecond Pulse Amplification. Pulse Shaping. Diagnostic Techniques. Measurement Techniques. Examples of Ultrafast Processes in Matter. Extreme Wavelengths. Phase Shifts Upon Transmission-Reflection. F's Dye Laser Configuration. Four Photon Coherent Interaction. Dyes and Solvents. Reconstruction of the Spectral Phase.
The polar distance transform by fast-marching

Image analysis tools that process the image using polar coordinates are needed to avoid the interpolation from polar to cartesian coordinates. We present a tool for analysing and processing circular objects - the polar distance transform computed by fast-marching. The fast marching method can be used for computing the grey-weighted distance transform by numerically approximating the Eikonal differential equation. We modify the Eikonal equation using weights that depend on the radius and angle relative to a pre-defined coordinate system.
Hypotheses, tests, methods, and innovation: The balancing act in research

Clinical and epidemiological research studies are essential to advancing our understanding of human health conditions such as epilepsy. To be of value, studies must not only address important questions, they must do so in such a way as to contribute usefully to existing knowledge. Comprehensive understanding of the subject matter is only one component of successful research and research reports. A firm grasp of research methods and statistical issues and a scholarly command of the relevant and current literature are essential components to high‐quality research. No single study, however, is perfect nor can any single study definitively answer a given single research question. Studies must be designed and evaluated in light of historical, scientific, pragmatic, and ethical realities that may constrain them. Selected but essential methodological and statistical concepts are discussed along with the need to balance innovation and novelty against methodological rigor.
From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models

Variability in single neuron models is typically implemented either by a stochastic Leaky-Integrate-and-Fire model or by a model of the Generalized Linear Model (GLM) family. We use analytical and numerical methods to relate state-of-the-art models from both schools of thought. First we find the analytical expressions relating the subthreshold voltage from the Adaptive Exponential Integrate-and-Fire model (AdEx) to the Spike-Response Model with escape noise (SRM as an example of a GLM). Then we calculate numerically the link-function that provides the firing probability given a deterministic membrane potential. We find a mathematical expression for this link-function and test the ability of the GLM to predict the firing probability of a neuron receiving complex stimulation. Comparing the prediction performance of various link-functions, we find that a GLM with an exponential link-function provides an excellent approximation to the Adaptive Exponential Integrate-and-Fire with colored-noise input. These results help to understand the relationship between the different approaches to stochastic neuron models.
RARELD for Loss Differentiation and Reduction in Wireless Network

Data communication in networking is done through exchanging the data packets between the end points. The packet loss may occur in wired/wireless network during the data transmission. Losses in wired network are caused because of congestion in routers, but losses in wireless network may occur because of interference or network congestion. The main problem in wireless network compared to wired network is classification of losses. Transport protocols are response to handle the losses, if the transmission uses existing protocols like TCP, the losses are always classified as congestion loss by sender, causing reduced throughput. Main aim of this paper is to reduce the losses and improve the throughput of wireless network. This can be achieved by using both loss differentiation and reduction method together to achieve high throughput. RARELD (Receiver Assisted Random Early Loss Differentiation) is a loss differentiation method used to classify type of loss and also used to reduce losses in wireless network. In this method both sender as well as receiver are used to optimize loss occurred in network.
Hardware Architecture for Stitching Images with Haze Removal Function

Clear and wide-angle images are essential to detect and trace objects for applications such as CCTV and unmanned vehicles. Fog, haze, and rain causes problem in getting clear image and lowers the detection rate of objects. To remove haze, we use median dark channel prior which can reduce the complexity of hardware. Image stitching stitches a number of images from multiple cameras into single wide-angle image like a panorama view. It is more efficient for surveillance to use single wide-angle image than multiple images. Stitched image is generated by several steps, feature point extraction, matching, and brightness correction. For real-time application, software implementation is not feasible due to its large complexity and long computation. Therefore, we exploit parallel processing architecture of hardware to implement haze removal and stitching. FPGA implementation shows that the performance is 30fps at 1280x720 resolution which is suitable for real-time applications.
Controversies in the management of the cutaneous T cell lymphomas

The primary cutaneous T cell lymphomas (CTCL) encompass all malignancies of the T cell where the skin is the primary organ of involvement. The diagnosis of a CTCL variant can be detoured by a number of obstacles including the slow evolution of the disease into a classic clinical and pathologic pattern. A realistic goal of early stage treatment is to reduce the likelihood of progression to a more advanced stage, not to achieve a cure. No studies have adequately compared the different systemic agents in patients with advanced CTCL so the clinician is left to act in the best interest of the patient with what evidence is available. When using the systemic agents, a “start low and go slow” strategy may offer patients several advantages. Dermatologists are uniquely trained to diagnose and to manage all but the most advanced stage patients with CTCL.
Movement Protein Pns6 of Rice dwarf phytoreovirus Has Both ATPase and RNA Binding Activities

Cell-to-cell movement is essential for plant viruses to systemically infect host plants. Plant viruses encode movement proteins (MP) to facilitate such movement. Unlike the well-characterized MPs of DNA viruses and single-stranded RNA (ssRNA) viruses, knowledge of the functional mechanisms of MPs encoded by double-stranded RNA (dsRNA) viruses is very limited. In particular, many studied MPs of DNA and ssRNA viruses bind non-specifically ssRNAs, leading to models in which ribonucleoprotein complexes (RNPs) move from cell to cell. Thus, it will be of special interest to determine whether MPs of dsRNA viruses interact with genomic dsRNAs or their derivative sRNAs. To this end, we studied the biochemical functions of MP Pns6 of Rice dwarf phytoreovirus (RDV), a member of Phytoreovirus that contains a 12-segmented dsRNA genome. We report here that Pns6 binds both dsRNAs and ssRNAs. Intriguingly, Pns6 exhibits non-sequence specificity for dsRNA but shows preference for ssRNA sequences derived from the conserved genomic 5′- and 3′- terminal consensus sequences of RDV. Furthermore, Pns6 exhibits magnesium-dependent ATPase activities. Mutagenesis identified the RNA binding and ATPase activity sites of Pns6 at the N- and C-termini, respectively. Our results uncovered the novel property of a viral MP in differentially recognizing dsRNA and ssRNA and establish a biochemical basis to enable further studies on the mechanisms of dsRNA viral MP functions.
Using Whole-Genome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster

Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melanogaster, using ∼2.5 million SNPs determined by sequencing the Drosophila Genetic Reference Panel population of inbred lines. We constructed a genomic relationship matrix from the SNP data and used it in a genomic best linear unbiased prediction (GBLUP) model. We assessed predictive ability as the correlation between predicted genetic values and observed phenotypes by cross-validation, and found a predictive ability of 0.239±0.008 (0.230±0.012) for starvation resistance (startle response). The predictive ability of BayesB, a Bayesian method with internal SNP selection, was not greater than GBLUP. Selection of the 5% SNPs with either the highest absolute effect or variance explained did not improve predictive ability. Predictive ability decreased only when fewer than 150,000 SNPs were used to construct the genomic relationship matrix. We hypothesize that predictive power in this population stems from the SNP–based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage disequilibrium with causal variants. We discuss the implications of these results for genomic prediction in other organisms.
Energy Transfer for Implantable Electronics in the Electromagnetic Midfield (Invited Paper)

(Invited Paper) Abstract—The wireless transfer of electromagnetic energy into the human body could power medical devices and enable new ways to treat various disorders. To control energy transfer, metal structures are used to generate and manipulate radio-frequency electromagnetic fields. Most systems for transfer across the biological tissue operate in the quasi-static limit, but operation beyond this regime could afford new powering capabilities. This review discusses some recent developments in the design and implementation of systems operating in the electromagnetic midfield, where transfer exploits wave-like fields in the body.
Extensions of the Cable Equation Incorporating Spatial Dependent Variations in Nerve Cell Diameter

Neuronal function relies on electrical signaling which, in turn, depends greatly on the morphology of neurons. Local changes in the diameter of neurites caused by deformations such as tumors or enlarged cerebral aneurysms may significantly affect electrical conduction. Morphologically accurate models are typically constructed by representing neurons as branched cables, where a system of coupled cable equations represents small neuronal segments as cylinders. Two extensions of the linear cable equation that allow the radius to change continuously in space are constructed here by assuming that the radius of a neuronal segment changes to form a section of a cone or a hyperbolic volume. The cable equation on a cone has an analytical solution involving modified Bessel functions. A more realistic representation of radial deformation is realized by writing the radius as a hyperbolic function of distance along the neurite. The steady state version of the hyperbolic equation is an adapted form of Ince’s equation. The cable equation with a hyperbolic radius has an analytical solution only in special conditions, and its numerical solutions can be qualitatively compared to the steady state solutions of the cable equation on a series of cylinders. Unlike the cases for the cylindrical and frustum shapes, the steady state solution of the equation on the hyperbolic cable describes a decay followed by an amplification of voltage along the cable. This counter-intuitive result suggests that non-uniformities in the diameter of neurites have local amplifying effects that may have a non-negligible impact on the propagation of signals in neurons. This research shows how different geometrical representations used to derive cable equations have varying impacts on the calculation of solutions.
Energy-efficient spatially-adaptive clustering and routing in wireless sensor networks

Wireless sensor networks hold the potential to open new domains to distributed data acquisition. However, low-cost battery-powered nodes are often used to implement such networks, resulting in tight energy and communication bandwidth constraints. Cluster-based data compression and aggregation helps to reduce communication energy consumption. However, neglecting to adapt cluster sizes to local network conditions has limited the efficiency of previous clustering schemes. We have found that sensor node distances and densities are key factors in clustering. To the best of our knowledge, this is the first work taking these factors into consideration when adaptively forming data aggregation clusters. Compared with previous uniform-size clustering techniques, the proposed algorithm achieves up to 24% communication energy savings in uniform density networks and 36% savings in non-uniform density networks.
Understanding the plant level costs and benefits of ERP: will the ugly duckling always turn into a swan?

This paper explores the impact of enterprise resource planning (ERP) systems using the individual manufacturing facility as the level of analysis. A model of ERP costs and benefits based on organizational information processing theory is proposed. The model resolves some of the apparently contradictory ERP impacts that have been reported in the trade literature. The paper then describes ERP implementations in two plants. Organizational information processing theory explains many of the costs and benefits that were observed in the cases. The cases also revealed several unexpected insights. Based on the case study findings, the paper proposes a revised model of ERP impacts.
Artificial Neural Network Based Fault Classifier for Transmission Line Protection

This paper presents a method for classification of transmission line faults based on Artificial Neural Network (ANN). Samples of prefault and postfault three phase currents taken at one end of transmission line are used as ANN inputs. Simulation studies have been carried out extensively on two power system models: one in which the transmission line is fed from one end and another, in which the transmission line is fed from two ends. Different types of faults at different operating conditions have been considered for carrying out simulation studies. The simulation results confirm the feasibility of the proposed approach.