Miracle-dz/UniFAR-FTUs huggingface dataset:
A dynamic indirect IP lookup based on prefix relationships

Internet is growing very rapidly in both the size and the traffic. Routers with efficient IP lookup schemes are needed to handle the amount of traffic. The IP lookup scheme is a key issue in design of IP routers, which searches the next hop of each incoming packet with a longest prefix matching in routing table. This operation is complex and is a major bottleneck in a high performance router. In this paper, we propose a fast and updateable scheme for dynamic routing tables that can dynamically insert and delete prefixes. We adopt a compressed bitmap data structure for IP lookup and develop a prefix parent relationship data structure for dynamically update prefixes. The simulation reveals that our scheme has high performance in IP lookup, even using real IPv4 routing tables with large entries.
Improving Face Recognition by Video Spatial Morphing

The focus of this chapter is in the problem of using technology to grant access to restricted areas by authorised persons, hereafter called ‘clients’, and to deny access to unauthorised or unidentified persons, the so called ‘impostors’. Conventional methods, such as magnetic or smart cards, user/password login and others, are being progressively recognised as insecure due to their many shortcomings, like the possibility of being lost, damaged or forged. Other methods, particularly those based on biometrics, are being increasingly used as they allow the verification of an individual’s identity on the basis of precise and careful measures of biological and physiological characteristics, such as fingerprints, hand and palm print geometry, iris and retina patterns, voice and face recognition. Automatic face recognition has very much progressed in the last few years, making its use practical in experimental or commercial systems. However, further research is still needed to make these systems more robust, reliable and less dependant on special constraints, particularly those imposed on the data acquisition process. In order to be as flexible as possible, current face recognition systems must use a large database of facial views for each client, so that distinct poses and emotional states can be accommodated, as well as other short-term variations in appearance caused by cosmetics or beard size, and by the use of various accessories such as spectacles or earrings. These multiple views are intended to increase the individual’s recognition rate for the capture of a single facial test image. The large dimension of the faces database induces a number of problems, namely the requirement for more storage, the increased computing time for recognition and decision, and the need for more complex classifiers. In an attempt to cope with the above problems we have devised an alternative approach, essentially consisting in keeping a much smaller facial image database, and in testing for valid matches a number of images extracted from a video fragment acquired during the person’s path in direction to the protected entrance. The main advantages to be expected from this approach can be summarised as: (a) the size of the reference face database is substantially reduced, as a single image or a small number of images for each individual are kept, (b) the clients are not subject to much discomfort when building the database, as a single neutral view (for each relevant appearance) is
Focal Deletion of the Adenosine A1 Receptor in Adult Mice Using an Adeno-Associated Viral Vector

Adenosine is a ubiquitous neuromodulator that increases sleep, inhibits seizures, and promotes neuroprotection. Many of these effects are mediated by A1 receptors, but A1 receptors are expressed in most brain regions, and distinguishing the precise site of action of adenosine is challenging. To test the role of adenosine in different hippocampal regions, we have used the Cre-loxP system and an adeno-associated viral (AAV) vector to focally delete endogenous adenosine A1 receptors in the hippocampus. Microinjection of an AAV vector containing the gene for Cre recombinase induced intense, focal, neuron-specific recombination in reporter mice. In a separate line of mice with loxP sites flanking the major coding exon for the adenosine A1 receptor, this AAV-Cre markedly reduced A1 receptor mRNA and focally abolished the postsynaptic response to adenosine without any change in basic electrophysiologic properties. Adenosine inhibits signaling between CA3 and CA1 neurons, but it is unclear from pharmacologic studies whether this response is caused by presynaptic or postsynaptic effects. Deletion of A1 receptors from CA3 neurons abolished this response to adenosine, but deletion of A1 receptors from CA1 neurons had no effect, demonstrating a presynaptic site of action. This transduction knock-out technique holds enormous potential for dissecting the functions of different CNS pathways.
A Double Auction Market for Computerized Traders !

SFI WORKING PAPER: 1989--001 SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at SFI, inspired by an invited visit to or collaboration at SFI, or funded by an SFI grant. ©NOTICE: This working paper is included by permission of the contributing author(s) as a means to ensure timely distribution of the scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the author(s). It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may be reposted only with the explicit permission of the copyright holder. www.santafe. edu
Object part segmentation and classification in range images for grasping

Recognition by Components (RBC) has been one of the most conceptually significant frameworks for modeling human visual object recognition. Extension of the model to practical robotic applications have been traditionally limited by the lack of good response in textureless areas in the case of conventional inexpensive stereo cameras as well as by the need for expensive laser based sensor systems to compensate for this deficiency. The recent availability of RGB-D sensors such as the PrimeSense sensor has opened new avenues for practical usage of these sensors for robotic applications such as grasping. In this paper, we present novel algorithms for segmentation of objects and parts from range images with extensions based on semantic cues to yield robust part detection. The detected parts are then parameterized using a superquadric based fitting framework and classified into one of different generic shapes. The categorization of the parts enables rules for grasping the object. This Grasping by Components (GBC) scheme is a natural extension of the RBC framework and provides a scalable framework for grasping of objects. This scheme also permits the grasping of novel objects in the scene, with at least one known grasp affordance. Results of the range segmentation are compared with another scene agnostic graph based segmentation approach.
Disproportionate minority contact in the juvenile justice system: An investigation of ethnic disparity in program referral at disposition

ABSTRACT Historically, minority youth have experienced harsher punishments and more negative outcomes than White youth even when risk assessment is used. The current study investigated the role of ethnicity in an understudied dispositional decision–program referral–and the outcomes associated with said referral using a sample of juvenile offenders (N = 2,678). The study used the Youth Level of Service/Case Management Inventory (YLS/CMI) to determine (1) if ethnicity predicted program referral when accounting for risk assessment and (2) if program referral predicted recidivism. Results indicated that ethnicity predicted program referral, and program referral predicted recidivism. Future directions for Disproportionate Minority Contact (DMC) research and implications for court officials are discussed.
Making Sense of Online Consumer Reviews: A Methodology

Online consumer reviews have become an increasingly important source of information for both consumers (i.e. about whether to buy) and marketers (i.e. about product strengths and weaknesses). However, online consumer reviews are unstructured and unsystematic in nature, making interpretation of these reviews an enormous challenge. The current paper sheds light on a particular methodology that can be used to investigate what consumers say about companies, brands or products. Consumer reviews of the four best-selling games available on Apple's App Store were compiled. Leximancer, a content analysis package, was used to compare comments from users who provided games with a five-star rating versus a one-star rating. Results from the Leximancer analysis reveal the most common themes and concepts that consumers use to describe their experience with these games. Specifically, five-star reviewers describe games as fun, awesome, amazing and addictive; one-star reviewers describe games as boring, easy and stupid. Additionally, negative reviews include themes regarding the presence of ads, technological difficulties and value. Future research should explore how consumers and marketers use this information.
Reading, Writing, and Revolution: Facilitating Social Activism in First Grade

ABSTRACT This article examines how teachers can develop a sense of social activism in students through critical multiculturalism. Drawing upon data from a nine-month participant observation study of a first-grade public charter school classroom in central California, this article highlights how teachers can integrate critical multiculturalism within an elementary classroom by connecting artists' lives to social issues to raise awareness of social injustices, promote critical reflection and consciousness, and develop a sense of social activism. Particular attention is paid to how teachers can incorporate this curriculum into their own classrooms.
Semantic personal image classification by energy expenditure

Semantic personal image classification is an attention problem in multimedia image retrieval. In our previous work [Chinpanchana, S et al., 2004], we classified semantic images into business, leisure, and sport categories by integrating the frequency pattern relationships between body parts and objects. However, the accuracy mainly depends on their objects. In the images that have high semantic complexities, the body movement play important solve on the meaning of image. In this paper, we present a new model to achieve more effective classifier called an energy expenditure model (EE). The EE model is based on the concept that human subjects in different classes of images are likely to spend different amounts of energy. The angular position and flexion forces are related into each body part. Experimental results show that the EE a can achieve an improvement of semantic images.
Graph-Cut Based Coscheduling Strategy Towards Efficient Execution of Scientific Workflows in Collaborative Cloud Environments

Recently, cloud computing has emerged as a promising computing infrastructure for performing scientific workflows by providing on-demand resources. Meanwhile, it is convenient for scientific collaboration since different cloud environments used by the researchers are connected through Internet. However, the significant latency arising from frequent access to large datasets and the corresponding data movements across geo-distributed data centers has been an obstacle to hinder the efficient execution of data-intensive scientific workflows. In this paper, we propose a novel graph-cut based data and task co scheduling strategy for minimizing the data transfer across geo-distributed data centers. Specifically, a dependency graph is firstly constructed from workflow provenance and cut into sub graphs according to the datasets which must appear in fixed data centers by a multiway cut algorithm. Then, the sub graphs might be recursively cut into smaller ones by a minimum cut algorithm referring to data correlation rules until all of them can well fit the capacity constraints of the data centers where the fixed location datasets reside. In this way, the datasets and tasks are distributed into target data centers while the total amount of data transfer between them is minimized. Additionally, a runtime scheduling algorithm is exploited to dynamically adjust the data placement during execution to prevent the data centers from overloading. Simulation results demonstrate that the total volume of data transfer across different data centers can be significantly reduced and the cost of performing scientific workflows on the clouds will be accordingly saved.
Linearisation:Adjuncts and arguments

Kayne (1994) introduced a ‘Linear Correspondence Axiom’ which offered for the first time a principled theory concerning the linear PF ordering of NL phrases and words. Within this theory, adjuncts constitute an anomaly, requiring a stipulative definition of c-command. If the category labels of Categorial Grammar are used, a simpler linearisation depending on Asymmetric Merge is obtained, which applies without stipulation to adjuncts. The linearisation makes some distinct and desirable predictions.
A comparison of two partial matching strategies for classification of unseen cases

This paper compares two partial matching strate- gies, selective and mixed, for classification of unseen cases. The selective partial matching is a novel approach for classification, while mixed partial matching was implemented in the LERS classification system several years ago. Though results of our experiments show that neither strategy is better than the other, an important conclusion is that it is crucial to implement both strategies since the correct choice of one of these strategies, for a specific data set, results in substantial improvement of the final classification.
Interaction in a Constructivist Classroom in a Malaysian Undergraduate ESL Setting

This paper reports findings from a study on interactions (both verbal and non-verbal) in Malaysian higher education ESL classrooms. It explores how social constructivist-based learning may be implemented within three classrooms under study where it was also explicitly being trialled. Particular attention was given to the role played by the teacher and students in terms of co-constructing knowledge. The study used qualitative methods and data were collected in the form of interviews (student and teacher participants), video recordings of classes in session and researcher’s field notes obtained through observation. However, for the purpose of this paper, these three types of data obtained from a single lesson were considered for analysis. Pre and post-interviews were held with the teacher and selected students after the video recording of a lesson. Findings suggested that the pedagogy implemented closely followed a collaborative learning model and had a positive impact on relationships between students and students/teacher within the classroom. Although some students exhibited reluctance to participate in this style of learning, generally both the teacher and the students were positive about the pedagogical approach and its impact on learning.
Clickstream Analysis: a Strategy to improve e-Business

E-business can be defined as the sale of services and goods via electronic channels or through internet for household or personal use by customers (1). This definition includes electronic commerce tasks that results in transactions of a business with end customers rather than business consumer i.e. business to consumer rather than business to business. In short, electronic business is one that is done with internet as a market place. With increasing business operations through internet, organizations have started adapting strategies to make their web pages competitive than that of their rivals. Clickstream is a process of analyzing, collecting and reporting data about visitor's path through the internet, including websites and the pages of sites that the user visited in what order and how long the visitor stayed on that page. Clickstream analysis helps organizations in tracking their customers and their preferences. This paper discusses in detail the contribution of clickstream analysis in promotion of e-business.
MINING VERY LARGE DATASETS WITH SVM AND VISUALIZATION

We present a new support vector machine (SVM) algorithm and graphical methods for mining very large datasets. We develop the active selection of training data points that can significantly reduce the training set in the SVM classification. We summarize the massive datasets into interval data. We adapt the RBF kernel used by the SVM algorithm to deal with this interval data. We only keep the data points corresponding to support vectors and the representative data points of non support vectors. Thus the SVM algorithm uses this subset to construct the non-linear model. We also use interactive graphical methods for trying to explain the SVM results. The graphical representation of IF-THEN rules extracted from the SVM models can be easily interpreted by humans. The user deeply understands the SVM models’ behaviour towards data. The numerical test results are obtained on real and artificial datasets.
Interactive query suggestion in Thai library automation system

This paper introduces interactive query expansion for Thai library automation system. The approach aims to expand original Thai-language user queries to a new set of more meaningful queries in the same language. The purpose is to improve the quality of Thai user query and suggest the user with a new set of more meaningful queries. User satisfaction with retrieved results should be improved. The approach employs the information in MARC 21 bibliographic format (title access field 245) in order to generate a new list of candidate terms. The genetic process is then augmented to select new expanded terms from those candidates. The approach is evaluated on the collection of Thai educational materials such as book, journal and magazine. The performance is measured by the accuracy of new expanded terms. We asked 87 participants whether our generated terms are related to their original query. The experiment shows that users quite agree that our technique is able to generate useful terms for searching their materials in library system.
Interrelations of Graph Distance Measures Based on Topological Indices

In this paper, we derive interrelations of graph distance measures by means of inequalities. For this investigation we are using graph distance measures based on topological indices that have not been studied in this context. Specifically, we are using the well-known Wiener index, Randić index, eigenvalue-based quantities and graph entropies. In addition to this analysis, we present results from numerical studies exploring various properties of the measures and aspects of their quality. Our results could find application in chemoinformatics and computational biology where the structural investigation of chemical components and gene networks is currently of great interest.
67 A Review of Patterns for Programming in the Cloud Julian Friedman 69 Ensurance to Assurance : Complex Information Management for Safety Critical Applications

Modern large-scale date centres (DCs), such as those used for cloud computing service provision, are becoming ever-larger as the operators of those data centres seek to maximise the benefits from economies of scale. DCs appear, from the outside at least, to be highly regimented and regularly-structured engineering arte-facts. Aisle after aisle of racks, each rack being a vertical frame housing a number of chasses, each chassis housing a regular arrangement of thin computer mother-board units: the “blade-servers” that make up the datacentre‟s computing infrastructure [6]. With the increases in size, however, comes a growth in system complexity, which is usually problematic. There is an increased desire for automated "self-star" configuration, management, and failure-recovery of the data-centre infrastructure, but many traditional techniques scale much worse than linearly as the number of nodes to be managed increases. As the number of nodes in a median-sized data-centre looks set to increase by two or three orders of magnitude in coming decades, it seems reasonable to attempt to explore and understand the scaling properties of the data-centre middle-ware before such data-centres are constructed. In a previous paper [1] we used the SPECI large-scale data-centre simulator [2] to compare the robustness and scaling characteristics of data-centres whose dependent components are connected via Strogatz-Watts small-world (SW) networks [3], versus those organized as Barabasi-Albert scale-free (SF) networks [4], and found significant differences. Based on that work we could show [15] that hybrid complex network topologies of dependencies between the computing components that make up a data-centre service may bring benefits. We emphasise the hybrid nature of the complex network topologies here because “non-standard” network constructions are best: these topologies are nonstandard in the sense that they are neither purely scale-free (SF) nor purely small-world (SW), but rather part-way between the two. The Klemm-Eguiliz (KE) construction method [5] was modified by applying techniques from [12] to generate directed complex network topologies for data-centre component dependencies. The KE model has a control parameter μ[0,1]R that determines whether the networks generated are SW (0<μ<<1) or SF (μ=1) or a “hybrid” network topology part-way between SW and SF (0<μ<1). We find that the best scores for system-level performance metrics of the simulated data-centres are given by “hybrid” values of μ significantly different from pure-SW or pure-SF. This document is based on work presented in [15].
Developmental Trajectories for Children With Dyslexia and Low IQ Poor Readers

Reading difficulties are found in children with both high and low IQ and it is now clear that both groups exhibit difficulties in phonological processing. Here, we apply the developmental trajectories approach, a new methodology developed for studying language and cognitive impairments in developmental disorders, to both poor reader groups. The trajectory methodology enables identification of atypical versus delayed development in datasets gathered using group matching designs. Regarding the cognitive predictors of reading, which here are phonological awareness, phonological short-term memory (PSTM) and rapid automatized naming (RAN), the method showed that trajectories for the two groups diverged markedly. Children with dyslexia showed atypical development in phonological awareness, while low IQ poor readers showed developmental delay. Low IQ poor readers showed atypical PSTM and RAN development, but children with dyslexia showed developmental delay. These divergent trajectories may have important ramifications for supporting each type of poor reader, although all poor readers showed weakness in all areas. Regarding auditory processing, the developmental trajectories were very similar for the two poor reader groups. However, children with dyslexia demonstrated developmental delay for auditory discrimination of Duration, while the low IQ children showed atypical development on this measure. The data show that, regardless of IQ, poor readers have developmental trajectories that differ from typically developing children. The trajectories approach enables differences in trajectory classification to be identified across poor reader group, as well as specifying the individual nature of these trajectories.