Miracle-dz/UniFAR-FTUs huggingface dataset:
The Switching Generator: New Clock-Controlled Generator with Resistance against the Algebraic and Side Channel Attacks

Since Advanced Encryption Standard (AES) in stream modes, such as counter (CTR), output feedback (OFB) and cipher feedback (CFB), can meet most industrial requirements, the range of applications for dedicated stream ciphers is decreasing. There are many attack results using algebraic properties and side channel information against stream ciphers for hardware applications. Al-Hinai et al. presented an algebraic attack approach to a family of irregularly clock-controlled linear feedback shift register systems: the stop and go generator, self-decimated generator and alternating step generator. Other clock-controlled systems, such as shrinking and cascade generators, are indeed vulnerable against side channel attacks. To overcome these threats, new clock-controlled systems were presented, e.g., the generalized alternating step generator, cascade jump-controlled generator and mutual clock-controlled generator. However, the algebraic attack could be applied directly on these new systems. In this paper, we propose a new clock-controlled generator: the switching generator, which has resistance to algebraic and side channel attacks. This generator also preserves both security properties and the efficiency of existing clock-controlled generators.
ExpertSeer: a Keyphrase Based Expert Recommender for Digital Libraries

We describe ExpertSeer, a generic framework for expert recommendation based on the contents of a digital library. Given a query term q, ExpertSeer recommends experts of q by retrieving authors who published relevant papers determined by related keyphrases and the quality of papers. The system is based on a simple yet effective keyphrase extractor and the Bayes' rule for expert recommendation. ExpertSeer is domain independent and can be applied to different disciplines and applications since the system is automated and not tailored to a specific discipline. Digital library providers can employ the system to enrich their services and organizations can discover experts of interest within an organization. To demonstrate the power of ExpertSeer, we apply the framework to build two expert recommender systems. The first, CSSeer, utilizes the CiteSeerX digital library to recommend experts primarily in computer science. The second, ChemSeer, uses publicly available documents from the Royal Society of Chemistry (RSC) to recommend experts in chemistry. Using one thousand computer science terms as benchmark queries, we compared the top-n experts (n=3, 5, 10) returned by CSSeer to two other expert recommenders -- Microsoft Academic Search and ArnetMiner -- and a simulator that imitates the ranking function of Google Scholar. Although CSSeer, Microsoft Academic Search, and ArnetMiner mostly return prestigious researchers who published several papers related to the query term, it was found that different expert recommenders return moderately different recommendations. To further study their performance, we obtained a widely used benchmark dataset as the ground truth for comparison. The results show that our system outperforms Microsoft Academic Search and ArnetMiner in terms of Precision-at-k (P@k) for k=3, 5, 10. We also conducted several case studies to validate the usefulness of our system.
Hierarchical exploration based active learning with support vector machine

The goal of active learning is to minimize the amount of labeled data required for machine learning. Some methods have focused on exploiting the samples with high uncertainty, but those methods fail in getting a representative set of the data samples. Other methods try to explore the representative samples by utilizing the prior distribution of the dataset. However, they are often computationally expensive and need a large amount of labeled data for initialization. In this paper we develop a hierarchical exploration based active learning algorithm that takes into account both the distribution of the dataset and the decision boundary of the current hypothesis. Our method uses the support vector machine (SVM) as the classifier. The hierarchical clustering algorithm is used to discover the dataset's structure step by step in a top-down manner. In each step of hierarchical structure discovery, the representative samples will be queried for labels to check the relative cluster's purity. The cluster with low purity will be divided further. After the draft SVM model is built with those representative samples, the uncertain samples near decision boundary will be further labeled if it can help reduce the entropy of the classifier. To show the effectiveness of the proposed method, our proposed method is compared with five state-of-art algorithms on six datasets from UCI. Our method shows the best performance through the comparison.
The Architecture of Concepts

Acknowledgments Introduction 1. On Concepts as Cultural Entities 2. ". the fundamental rights and liberties of mankind . ": The Architecture of the Rights of Mankind 3. ". there are, thank God, natural, inherent and inseparable rights as men . ": The Architecture of American Rights 4. ". the rights of man were but imperfectly understood at the revolution": The Architecture of Rights of Man 5. The Futures of Human Rights Index
Virtual actuator control

Robots typically have an individual actuator at each joint which can result in a nonintuitive and difficult control problem. In this paper we present a control method in which the real joint actuators are used to mimic virtual actuators which can be more intuitive and hence make the control problem more straightforward. Our virtual actuator control method requires a solution to the force distribution problem when applied to parallel mechanisms. An extension of Gardner's partitioned actuator set control method (1991) is presented. This extended method allows for dealing with constrained degrees of freedom in which the torque cannot be specified but can be measured. A simulated hexapod robot was developed to test the proposed control method. The virtual actuators allowed textbook control solutions to be used in controlling this highly nonlinear, parallel mechanism. Using a simple linear control law, the robot walked while simultaneously balancing a pendulum and tracking an object.
Alpha-Herpesvirus Infection Induces the Formation of Nuclear Actin Filaments

Herpesviruses are large double-stranded DNA viruses that replicate in the nuclei of infected cells. Spatial control of viral replication and assembly in the host nucleus is achieved by the establishment of nuclear compartments that serve to concentrate viral and host factors. How these compartments are established and maintained remains poorly understood. Pseudorabies virus (PRV) is an alpha-herpesvirus often used to study herpesvirus invasion and spread in the nervous system. Here, we report that PRV and herpes simplex virus type 1 infection of neurons results in formation of actin filaments in the nucleus. Filamentous actin is not found in the nucleus of uninfected cells. Nuclear actin filaments appear physically associated with the viral capsids, as shown by serial block-face scanning electron micropscopy and confocal microscopy. Using a green fluorescent protein-tagged viral capsid protein (VP26), we show that nuclear actin filaments form prior to capsid assembly and are required for the efficient formation of viral capsid assembly sites. We find that actin polymerization dynamics (e.g., treadmilling) are not necessary for the formation of these sites. Green fluorescent protein-VP26 foci co-localize with the actin motor myosin V, suggesting that viral capsids travel along nuclear actin filaments using myosin-based directed transport. Viral transcription, but not viral DNA replication, is required for actin filament formation. The finding that infection, by either PRV or herpes simplex virus type 1, results in formation of nuclear actin filaments in neurons, and that PRV infection of an epithelial cell line results in a similar phenotype is evidence that F-actin plays a conserved role in herpesvirus assembly. Our results suggest a mechanism by which assembly domains are organized within infected cells and provide insight into how the viral infectious cycle and host actin cytoskeleton are integrated to promote the infection process.
Molecular and Functional Characterization of theRhodopseudomonas palustris No. 7 Phosphoenolpyruvate Carboxykinase Gene

ABSTRACT The pckA gene, encoding the gluconeogenic enzyme phosphoenolpyruvate carboxykinase (PEPCK), was cloned by PCR amplification from the purple nonsulfur bacteriumRhodopseudomonas palustris No. 7. Sequencing of a 2.5-kb chromosomal SmaI-PstI fragment containing the structural gene revealed an open reading frame encoding 537 amino acids, homologous to known pckA genes. Primer extension analysis identified a transcriptional start site 72 bp upstream of thepckA initiation codon and an upstream sequence similar to ς70 promoters. Studies of a pckA-lacZ gene fusion indicated that when cells were grown in minimal media with various carbon sources, such as succinate, malate, pyruvate, lactate, or ethanol, under both anaerobic light and aerobic dark conditions, thepckA gene was induced in log phase, irrespective of the carbon source. A R. palustris No. 7 PEPCK-deficient strain showed growth characteristics identical to those of the wild-type strain either anaerobically in the light or aerobically in the dark when a C4-dicarboxylic acid, such as succinate or malate, was used as a carbon source. These results indicate that in R. palustris No. 7, an alternative gluconeogenic pathway may exist in addition to PEPCK.
Exploiting Semantic Product Descriptions for Recommender Systems

Content-driven and hybrid recommender systems propose productstocustomersmakinguseofdescriptivefeaturesand behavioral patterns, likewise. While most approaches exploit classical information retrieval techniques, e.g., nearestneighbor queries in metric spaces, availability and usage of richer semantic meta-information about products may further improve recommendation quality significantly. Massive taxonomies for product classification are coming of age, e.g, the United Nations Standard Products and Services Classification (UNSPSC), as well as proprietary standards, such as Amazon.com’s classification taxonomies for books, DVDs, CDs, and apparel. We exploit suchlike semantic background knowledge in order to leverage powerful inference opportunities for making user profiles, based upon the products these latter customers purchased, more meaningful. Ample empirical analysis, both offline and online, demonstrates our proposal’s superiority over common existing approaches when user information becomes sparse and implicit ratings prevail.
Peer-to-peer and distributed search in fixed and mobile environments

Distributed search has become a very important research area in light of the proliferation of peer-to-peer (P2P) networks. The convergence of fixed and mobile networks increases node dynamics further and adds to the complexity of the problem. This paper presents an exhaustive two-dimensional design space and a comprehensive survey of search designs. In the functional dimension, it identifies a series of mappings and integrated approaches like keyword lookup or semantic routing. In the structural dimension, design options for each mapping include computational approaches, central or completely replicated tables, classic or symmetric hierarchies, ordered spaces, and random structures and topologies. An evaluation of these design options serves as a guideline for system designers. Finally, it enables us to derive general implications and recommendations for P2P search design.
Chemical Characterization and Antioxidant, Antimicrobial, and Anti-Inflammatory Activities of South Brazilian Organic Propolis

South Brazilian organic propolis (OP), which has never been studied before, was assessed and its chemical composition, scavenging potential of reactive oxygen species, antimicrobial and anti-inflammatory activities are herein presented. Based on the chemical profile obtained using HPLC, OP was grouped into seven variants (OP1–OP7) and all of them exhibited high scavenging activity, mainly against superoxide and hypochlorous acid species. OP1, OP2, and OP3 had the smallest minimal inhibitory concentration (MIC) against Gram-positive bacteria Streptococcus mutans, Streptococcus oralis, and Streptococcus aureus (12.5–100 μg/mL). OP1, OP2, OP3, and OP4 were more effective against Pseudomonas aeruginosa (Gram-negative), with MIC values ranging from 100 to 200 μg/mL. OP6 showed anti-inflammatory activity by decreasing NF-kB activation and TNF-α release in RAW 264.7 macrophages, and expressing the NF-κB-luciferase reporter stable gene. Therefore, south Brazilian OP can be considered an excellent source of bioactive compounds with great potential of application in the pharmaceutical and food industry.
Searching digital music libraries 1 Running head: SEARCHING DIGITAL MUSIC LIBRARIES Searching digital music libraries

There has been a recent explosion of interest in digital music libraries. In particular, interactive melody retrieval is a striking example of a search paradigm that differs radically from the standard full-text search. Many different techniques have been proposed for melody matching, but the area lacks standard databases that allow them to be compared on common grounds—and copyright issues have stymied attempts to develop such a corpus. This paper focuses on methods for evaluating different symbolic music matching strategies, and describes a series of experiments that compare and contrast results obtained using three dominant paradigms. Combining two of these paradigms yields a hybrid approach which is shown to have the best overall combination of efficiency and effectiveness. Searching digital music libraries 3 Searching digital music libraries Corresponding author: David Bainbridge Phone: +64 7 838 4407 Fax: +64 7 858 5095 E-mail: d.bainbridge@cs.waikato. ac.nz A shorter version of this paper was presented at International Conference on Asian Digital Libraries, Singapore, December 2002.
A Method for Joint Compressing and Recovering Destructed Signals in Wireless Multimedia Sensor Networks

The WSNs are developed to sense, gather, process and transmit the real-world information and so in recent years, solving numerous challenges of wireless sensor networking are considered intensely. In addition, in Wireless Multimedia Sensor Networks intraand intersignal correlations can be exploited in the theory of distributed source coding and similarly in distributed compressive sensing to compress signals as much as possible. These cases may be occurred in applications and services in which work with smart spaces and context aware networks.In this paper based on compressive sensing a framework denoted asECMis proposed to compress and reconstruct the signals of the sensors even for networks which the data transmission is imperfect.ECM uses the concepts of distributed compressive sensing and the shared information between sensor's signals to compress the signals more.
Design of microwave modules for microstrip antenna using Advanced Design System (ADS)

A transceiver is a device consisting of both a transmitter and a receiver that are combined and share common circuitry. Thus, to perform this approach we obtain 5 designs. They are: 1. Power Amplifier 2. Driver Amplifier. 3. Fractal Filter Design. 4. Modified Fractal Filter Design. 5. Duplexer Design. The design 1 and 2 form the chain design of power amplifier required at the transmitter section. The design 3 and 4 form a part of receiver. To connect this transmit and receive section to a single antenna, a duplexer that is design 5 is used. These amplifiers are designed using GaN HEMT transistors and fabricated. These two designs of filters and the duplexer are fabricated using the specified substrate. All these designs were tested using the required test set-up. The proposed designs have been designed using the software Advanced Design System (ADS). These designs are implemented in the frequency range of 2.2 GHz to 2.3 GHz.
Webpage Aesthetics: One Size Doesn't Fit All

Webpages vary drastically in their look and feel: the presence of images is a major discriminating factor. Some webpages contain mainly text; others exploit flashy ads and a variety of eye-catching pictures. In this paper, we investigate the impact of graphics on webpage aesthetics perception and computation. We split webpages in three categories -- small, moderate and high amount of graphics -- and analyzed how different visual features predicted aesthetics for the different categories. Significant between-category differences were found, e.g., the amount of white space decreased aesthetics for the high-graphic webpages, but not for other webpages; more on-page main colors increased aesthetics for the low-graphic webpages, but decreased for the high-graphic webpages. We suggest future research investigates separately webpages with low and high graphic amount. No single improvement recipe may exist for all webpages; a more fruitful strategy would be suggesting different improvements for different types of webpages.
The neglected tool in the Bayesian ecologist's shed: a case study testing informative priors' effect on model accuracy

Despite benefits for precision, ecologists rarely use informative priors. One reason that ecologists may prefer vague priors is the perception that informative priors reduce accuracy. To date, no ecological study has empirically evaluated data-derived informative priors' effects on precision and accuracy. To determine the impacts of priors, we evaluated mortality models for tree species using data from a forest dynamics plot in Thailand. Half the models used vague priors, and the remaining half had informative priors. We found precision was greater when using informative priors, but effects on accuracy were more variable. In some cases, prior information improved accuracy, while in others, it was reduced. On average, models with informative priors were no more or less accurate than models without. Our analyses provide a detailed case study on the simultaneous effect of prior information on precision and accuracy and demonstrate that when priors are specified appropriately, they lead to greater precision without systematically reducing model accuracy.
Analysis of robustness of attributes selection applied to speech emotion recognition

The paper presents the analysis of the robustness of an attributes selection method applied to speech emotion recognition. The features used were extracted by the front-end ETSI Aurora eXtended of a mobile terminal in compliance with the ETSI ES 202 211 V1.1. 1 standard. On the basis of the time trend of these parameters, over 3700 statistical attributes were extracted to characterize semantic units of varying length (sentences, words and generic chunks). Using the WEKA (Waikato Environment for Knowledge Analysis) software the most significant attributes for the classification of two emotional states were selected using the CFSSubsetEval-BestFirst method. The results of classification, obtained using NaiveBayes models, were obtained using intra-corpus and inter-corpora experiments on four different speech corpora performing 4000 trainings and tests. On the basis of these results we can study the robustness of the attributes selection method.
Nitrogen and carbon isotope variability in the green-algal lichen Xanthoria parietina and their implications on mycobiont–photobiont interactions

Stable isotope patterns in lichens are known to vary largely, but effects of substrate on carbon and nitrogen stable isotope signatures of lichens were previously not investigated systematically. N and C contents and stable isotope (δ15N, δ13C) patterns have been measured in 92 lichen specimens of Xanthoria parietina from southern Bavaria growing on different substrates (bark and stone). Photobiont and mycobiont were isolated from selected populations and isotopically analyzed. Molecular investigations of the internal transcribed spacer of the nuclear ribosomal DNA (ITS nrDNA) region have been conducted on a subset of the specimens of X. parietina. Phylogenetic analysis showed no correlation between the symbionts X. parietina and Trebouxia decolorans and the substrate, isotope composition, or geographic origin. Instead specimens grown on organic substrate significantly differ in isotope values from those on minerogenic substrate. This study documents that the lichens growing on bark use additional or different N sources than the lichens growing on stone. δ15N variation of X. parietina apparently is controlled predominantly by the mass fraction of the mycobiont and its nitrogen isotope composition. In contrast with mycobionts, photobionts of X. parietina are much more 15N-depleted and show less isotopic variability than mycobionts, probably indicating a mycobiont-independent nitrogen acquisition by uptake of atmospheric ammonia.
Improving the performance of the enterprise information system via optimal scheduling

Many companies use business intelligence (BI) to improve organization performance. BI relies on the data in the enterprise information system (EIS), and in order to guarantee data freshness, this data needs periodic updating. The EIS contains data warehouses frequently residing in the many machines in at least one data center. Many jobs, also called tasks or operations, run to bring data into the data warehouses. Some jobs may depend on others, whereas others may be independent. Traditionally, these jobs are scheduled based on the dependency but not on the critical path; thus, the conventional scheduling algorithm is nonoptimal. This leads to excessive runtime and nonoptimal usage of resources. Data processing jobs in the data warehouse system involve many resources, so it is necessary to find the best job-scheduling methodology. According to existing literature, job scheduling in the distributed data warehouse system has not been studied. This paper discusses how to use the historic data and critical path to generate an optimal scheduling plan involving the minimum number of machines while guaranteeing shortest runtime. Experimental results show the optimal scheduling algorithm can reduce runtime and optimize usage of resources.