Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Linear Hull in Matsui's Algorithm 1 (extended version)

We consider linear approximations of an iterated block cipher in the presence of several strong linear approximation trails. The eect of such trails in Matsui's Algorithm 2, also called the linear hull eect, has been previously studied by a number of authors. However, the eect on Matsui's Algorithm 1 has not been investigated until now. In this paper, we ll this gap and examine how to exploit the linear hull in Matsui's Algorithm 1. We develop the mathematical framework for this kind of attacks. The complexity of the attack increases with the number of strong linear trails. We show how to reduce the number of trails and thus the complexity using related keys. Further, we illustrate our theory by experimental results on a reduced round version of the block cipher PRESENT.
Integration of magnetic components in a step-up converter for fuel cell

Fuel cell generator is a good example of a high voltage gain application in which dc-dc step-up converter is a critical part. The input voltage of the converter decreases while the output power increases. It creates challenges in design of the converter's magnetic components. Scope of this paper is integration of the dc inductor and the transformer on a single core. Such integration improves utilization of the core and windings. It leads to size reduction of the converter.
Mobile recommendation system for cosmeceutical products

Most cosmeceutical products show ingredients in English, which is not easy to understand for consumers. Consumers can link to some existing websites and type these English ingredients to inquiry. However, these English ingredients are usually long and complex, and consumers are prone to making error on typing. Except for ingredients, information about similar commodity is also important for consumers when making decisions on purchasing cosmeceutical products. The purpose of this research is to provide an ingredient retrieval and recommendation system for cosmeceutical products. Our research utilizes 2D barcode to automatically gather ingredients and vector model to search f or commodities with similar ingredients. Our system is implemented on Android mobile phone, so the retrieval and recommendation are prompt.
Foreign Exchange Rates Forecasting with Neural NetworksJingtao

| In this paper, a neural network based foreign exchange rates forecasting method is discussed. Neural networks with time series and technical indicators as inputs are built to capture the underlying \rules" of the movement in currency exchange rates. Before using historical data to train the neural networks, the traditional R/S analysis is used to test the \eeciency" of each market. The study shows that without the use of extensive market data or knowledge, useful prediction can be made and signiicant paper proot can be achieved with simple technical indicators.
Multi Label Spatial Semi Supervised Classification using Spatial Associative Rule Mining and Evolutionary Algorithms

Multi-label spatial classification based on association rules with multi objective genetic algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal with multiple class labels problem. In this paper we adapt problem transformation for the multi label classification. We use hybrid evolutionary algorithm for the optimization in the generation of spatial association rules, which addresses single label. MOGA is used to combine the single labels into multi labels with the conflicting objectives predictive accuracy and comprehensibility. Semi supervised learning is done through the process of rule cover clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is simulated and the results are compared with MOGA based associative classifier, which out performs the existing.
Origin of Circulating Free DNA in Sepsis: Analysis of the CLP Mouse Model

Recently, it has been reported that circulating free DNA (cf-DNA) in the blood is increased in various infectious diseases, including sepsis. Moreover, a relationship between cf-DNA and neutrophil extracellular traps (NETs) has been suggested. However, it is still unclear what the source and physiological role of cf-DNA in sepsis are. In this study, we examined the source of cf-DNA by detecting citrullinated histone H3, a characteristic feature of NET formation, in cecal ligation and puncture- (CLP-)operated mice. In addition, neutrophil depletion using anti-Ly6G antibodies was performed to assess the association between neutrophils and cf-DNA. Increased cf-DNA levels were observed only in CLP mice and not in the control groups; the qPCR findings revealed that the cf-DNA was mainly host-derived, even in bacteremic conditions. Citrullinated histone H3 was not increased in the neutrophils upon CLP, and the depletion of neutrophils showed limited effects on decreasing the amount of cf-DNA. Taken together, these results suggested that elevated cf-DNA levels during early-phase sepsis may represent a candidate biomarker for the severity of sepsis and that, contrary to previous findings, cf-DNA is not derived from neutrophils or NETs.
A Novel Humanized GLP-1 Receptor Model Enables Both Affinity Purification and Cre-LoxP Deletion of the Receptor

Class B G protein-coupled receptors (GPCRs) are important regulators of endocrine physiology, and peptide-based therapeutics targeting some of these receptors have proven effective at treating disorders such as hypercalcemia, osteoporosis, and type 2 diabetes mellitus (T2DM). As next generation efforts attempt to develop novel non-peptide, orally available molecules for these GPCRs, new animal models expressing human receptor orthologs may be required because small molecule ligands make fewer receptor contacts, and thus, the impact of amino acid differences across species may be substantially greater. The objective of this report was to generate and characterize a new mouse model of the human glucagon-like peptide-1 receptor (hGLP-1R), a class B GPCR for which established peptide therapeutics exist for the treatment of T2DM. hGLP-1R knock-in mice express the receptor from the murine Glp-1r locus. Glucose tolerance tests and gastric emptying studies show hGLP-1R mice and their wild-type littermates display similar physiological responses for glucose metabolism, insulin secretion, and gastric transit, and treatment with the GLP-1R agonist, exendin-4, elicits similar responses in both groups. Further, ex vivo assays show insulin secretion from humanized islets is glucose-dependent and enhanced by GLP-1R agonists. To enable additional utility, the targeting construct of the knock-in line was engineered to contain both flanking LoxP sites and a C-terminal FLAG epitope. Anti-FLAG affinity purification shows strong expression of hGLP-1R in islets, lung, and stomach. We crossed the hGLP-1R line with Rosa26Cre mice and generated global Glp-1r−/− animals. Immunohistochemistry of pancreas from humanized and knock-out mice identified a human GLP-1R-specific antibody that detects the GLP-1R in human pancreas as well as in the pancreas of hGLP-1r knock-in mice. This new hGLP-1R model will allow tissue-specific deletion of the GLP-1R, purification of potential GLP-1R partner proteins, and testing of novel therapeutic agents targeting the hGLP-1R.
The Role of Abduction in Automatic Storytelling

Some researches state that discovering what to say is part of the writing process. We are interested in studying this aspect of writing within the scope of computer models. MEXICA is a computer model for plot generation which, inspired by the idea of the discovering aspect of writing, avoids the use of predefined story-structures and explicit characters’ goal. This work claims that abduction is an important part of the process of discovering what to say and therefore computers models of abduction for automatic plot generation are an important topic to be studied. This paper describes some basic concepts about abduction and how they are implemented within the MEXICA program.
Unified IoT ontology to enable interoperability and federation of testbeds

After a thorough analysis of existing Internet of Things (IoT) related ontologies, in this paper we propose a solution that aims to achieve semantic interoperability among heterogeneous testbeds. Our model is framed within the EU H2020's FIESTA-IoT project, that aims to seamlessly support the federation of testbeds through the usage of semantic-based technologies. Our proposed model (ontology) takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite, Time, and DUL. In addition, we also introduce a set of tools that aims to help external testbeds adapt their respective datasets to the developed ontology.
Bifurcation analysis in a discrete SIR epidemic model with the saturated contact rate and vertical transmission

The aim of paper is dealing with the dynamical behaviors of a discrete SIR epidemic model with the saturated contact rate and vertical transmission. More precisely, we investigate the local stability of equilibriums, the existence, stability and direction of flip bifurcation and Neimark-Sacker bifurcation of the model by using the center manifold theory and normal form method. Finally, the numerical simulations are provided for justifying the validity of the theoretical analysis. c ©2016 All rights reserved.
vMON: An Efficient Out-of-VM Process Monitor for Virtual Machines

Cloud computing service has been evolved in providing a whole virtual data center from selling scattered virtual machines (VMs). Process Monitoring of a VM is a fundamental feature to guarantee the security of the virtual data center because of the rapid growth of the malware. Existing approaches are mainly based on virtual machine introspection (VMI) technique to isolate the monitor out-of-vm and designed to inspect the VM internal processes. However, few of them consider the real time control of process execution in the VMs, such as process termination or files operation conducted by the process. Early VMI-based solutions relied on some specific OS kernel data structures, so they need to know the OS information in advance instead of identifying the OS version at runtime for operating system compatible. In this paper, we propose a novel out-of-the-box process monitor named vMON, which can not only identify different guest OS versions and reconstruct rich semantic information for the target VM processes at runtime, but also control the behaviors of processes with fine granularity. In addition, vMON provides uniform programming interfaces to support the development of application-level security tools. A prototype of vMON has been implemented in kernel-based virtual machine (KVM) hyper visor, and its effectiveness and performance have also been evaluated through several experiments. The results show that vMON can successfully identify, analyze and control the behaviors of the processes in Guest OS with acceptable performance overhead. vMon incurs 0.74%~10.20% I/O overhead and 0.003s average interface return time.
Paternal Relatedness Predicts the Strength of Social Bonds among Female Rhesus Macaques

Forming strong, equitable, and enduring social bonds with a few individuals in a group carries adaptive benefits in terms of increased longevity, offspring survival and paternity success in birds and mammals, including humans. These recent insights generated a new interest in the factors creating variation in the strength of social relationships. Whether and how animals discriminate paternal kin from non-kin and bias their social behavior accordingly is being debated. This study explores the relative importance of dominance rank, age, maternal and paternal relatedness in shaping dyadic affiliative relationships in a group of 30 captive rhesus macaque females. The strength of social relationships, measured by the composite sociality index from observational data, was independently predicted in GLMMs by both maternal and paternal relatedness as well as rank similarity. In addition, social bonds between paternal half-sisters were stronger than between distantly related kin suggesting that females biased their affiliative effort towards paternal relatives. Despite identical relatedness coefficients bonds between mothers and their daughters were three times as strong as those between full sisters. Together these results add to the growing body of evidence for paternal kin biases in affiliative behavior and highlight that females prefer descendent over lateral kin.
SAND, a New Protein Family: From Nucleic Acid to Protein Structure and Function Prediction

As a result of genome, EST and cDNA sequencing projects, there are huge numbers of predicted and/or partially characterised protein sequences compared with a relatively small number of proteins with experimentally determined function and structure. Thus, there is a considerable attention focused on the accurate prediction of gene function and structure from sequence by using bioinformatics. In the course of our analysis of genomic sequence from Fugu rubripes, we identified a novel gene, SAND, with significant sequence identity to hypothetical proteins predicted in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans, a Drosophila melanogaster gene, and mouse and human cDNAs. Here we identify a further SAND homologue in human and Arabidopsis thaliana by use of standard computational tools. We describe the genomic organisation of SAND in these evolutionarily divergent species and identify sequence homologues from EST database searches confirming the expression of SAND in over 20 different eukaryotes. We confirm the expression of two different SAND paralogues in mammals and determine expression of one SAND in other vertebrates and eukaryotes. Furthermore, we predict structural properties of SAND, and characterise conserved sequence motifs in this protein family.
Assessment of swallowing in preterm newborns fed by bottle and cup.

PURPOSE To compare the swallowing performance of premature infants using a cup and a bottle during the first offer of food by mouth. METHODS This study was carried out with preterm newborns who presented low weight at birth and no neurological illnesses, genetic syndromes or congenital malformations. The newborns were assessed by videofluoroscopy while using a cup and a bottle, when they reached a post-conceptual age of ≥34 weeks, weight ≥ 1,500 g and showed signs of readiness for oral feeding. All children were fed exclusively by gavage during the period prior to the study. RESULTS This study included 20 preterm newborns, with average birth weight of 1,356 g and gestational age of 31.3 weeks. The majority of the bottle-fed newborns (68%) presented strong and rhythmic suction and 63% showed good sucking/swallowing/breathing coordination. The same percentage of newborns fed by cup (68%) could not perform the sipping movement and only 32% could suck a minimal amount of liquid contrast. There were no signs of laryngeal penetration and tracheal aspiration in both procedures. CONCLUSION At the first oral feeding, preterm newborns showed better swallowing performance with a bottle in comparison to using a cup.
Enhancing the Performance of Mobile Traffic Identification with Communication Patterns

Traffic classification is important especially for managing and monitoring networks which contain a wide variety of traffic, such as in the mobile network. Using only packet based feature in traditional classification is not enough for classifying mobile application traffic because of the complexity of mobile traffic. Therefore, this study proposes the technique that combines the packet size distribution and communication patterns extracted via graph let for identifying mobile application. The technique is robust to the complexity of mobile traffic and has no privacy concerns. Validation results over five popular mobile applications (Facebook, Line, Skype, You Tube, and Web) demonstrate that our combined method achieves high performance (0.95) of F-measure even using only randomly sampled 50 packets during 3-minute time interval. Moreover, the combination of these features distinguishes various applications with similar characteristics such as Facebook and Web.
Statistical selection of relevant objective criteria for speech enhancement assessment

The quality of denoised speeches is ideally evaluated using subjective listening tests. But, to facilitate the heavy workload, objective criteria represent an efficient alternative. Hence, many criteria are developed in the literature and more than one criterion is usually used. In this paper, we propose to address the problem of classical objective criteria choice to select those which are relevant for evaluating subjective quality of denoised signals. Two tools are used: the boxplots to discard criteria leading to a confusion when trying to discriminate between the five classes of MOS criteria and the Principal Component Analysis (PCA) to eliminate redundancy between criteria and to reduce dimensionality.
Coverage of bombings for political advantage: Turkish on-line news reporting of the 2016 Ankara attacks

ABSTRACT On 17 February 2016, a suicide bomber blew himself up, killing 28 people and injuring another 61 in the “heart” of Turkey’s capital Ankara. A few hours after the attack, the Turkish government blamed Salih Neccar from the (mostly) Kurdish-Syrian People’s protection Unit (YPG). Two days later, the Kurdistan Freedom Hawks (TAK) claimed responsibility and named the bomber as Abdülbaki Sömer a Turkish citizen. The bombing is part of a resumption of violence in Turkey between Turkish government authorities and Kurdish groups. In this paper, we examine how on-line news stories recontextualise the bombing. We assert that news sources multimodally recontextualise the bombing in ways which are advantageous to the news organisations’ owners, political alliances and supporters. By each news source representing their political interests unquestionably positive and opposition unconditionally negative, polarisation in Turkish politics is articulated. This does nothing to solve problems and heal wounds in a time of national crisis.
Emulation-based Evaluation of an Architecture for Wide-Area Service Composition

Service composition provides a flexible way to quickly enable new application functionalities using component services. We focus on the scenario where next generation portal providers “compose” the services of other providers. We have developed an architecture based on an overlay network of service clusters to provide failure-resilient composition of services across the wide-area Internet: our algorithms detect and recover quickly from failures in composed client sessions. In this paper, we present an evaluation of our architecture whose overarching goal is quick recovery of client sessions. The evaluation of an Internet-scale system like ours is challenging. Simulations do not capture true workload conditions and Internetwide deployments are often infeasible. We have developed an emulation platform for our evaluation – one that allows a realistic and controlled design study. Our experiments show the effectiveness of our recovery mechanisms: over 90% of the client sessions are restored within 1sec after failure detection in Internet paths. We collect trace data to show that failure detection itself can be tight on wide-area Internet paths – within about 2sec. Failure detection and recovery within these time bounds represents a significant improvement over existing Internet path recovery mechanisms that take several tens of seconds to a few minutes [12]. Furthermore, the control overhead involved in implementing our recovery mechanism is minimal in terms of network as well as processor resources; minimal additional provisioning is required for this.
Pianist Motion Capture with the Kinect Depth Camera

Sensing pianist movements is useful for various applications fields such as electronic music, music performance research, musician medicine, and piano pedagogy. This paper contributes an unobtrusive, markerless method to capture pianist movements based on depth sensing. The method was realized using the Kinect depth camera and evaluated in comparison with 2D marker tracking.
Escape from Winchester Mansion - Toward a Set of Design Principles to Master Complexity in IT Architectures

Although the management of complexity is a central task of CIOs and IT architects, in-depth examinations and the development of design theory in this area is, to the best of our knowledge, underrepresented in existing IS literature. Especially theory-based guidelines and information systems for the management of IT architecture complexity are missing. In a joint team of practitioners and researchers, we applied the action design research (ADR) method in order to tackle this class of problems, i.e., IT architecture complexity management. We derived a set of seven design principles (that guide the design of an information system that supports IT architects to manage IT architecture complexity), which we evaluated and enriched during multiple ‘building, intervention and evaluation’ cycles, according to ADR. In addition, we simultaneously implemented and evaluated a material artifact (i.e., a piece of software) for IT architecture complexity management.
Use of Transaction Cost Economics Framework to Study Information Technology Sourcing: Over-Application or Under-Theorizing?

IS discipline has witnessed significant growth in research on IT inand out-sourcing. Economic theories inform a large portion of this work. This paper examines the use of Transaction Cost Economics (TCE) as a theoretical lens to study IT sourcing decisions (internal vs. external). It reviews TCEâs major assumptions and assesses its key constructs: frequency, asset specificity, opportunism, and uncertainty, with regard to how they have been operationalized and what results have been obtained. This is done through a review of selected body of outsourcing literature. There is evidence of the impacts of production costs on outsourcing decisions, while findings with regard to transaction costs are inconclusive. In order to move the research agenda ahead we suggest examining the co-evolution of drivers for buyers and vendors to outsource, instead of just exploring cost determinants. We recommend that researchers take a longitudinal process perspective to improve our understanding of complex governance phenomena.