Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a cost-effective OpenFlow testbed for small-scale Software Defined Networking

OpenFlow is the first standard interface for realizing Software-Defined Networking (SDN) that can decouple the data and control plane to provide scalable network management. To validate the performance and features of the OpenFlow standard, many researchers have utilized specialized hardware network devices such as NetFPGA. However, these devices are not suitable for implementing a small-scale SDN testbed due to high cost, complexity, and specialized programming languages. The well-known SDN emulator, Mininet[l], is also widely utilized but it is not enough to support network dynamicity and the performance of the virtualized hosts. In this paper, we suggest a more cost-effective alternative of implementing SDN testbed with Open vSwitch (OVS), based on the Raspberry-Pi that is a low-cost embedded Linux machine. We validate our testbed with the OpenFlow specification 1.0 and prove that its maximum network throughput shows almost the same performance compared to the NetFPGA-1G.
Effective potential and spontaneous symmetry breaking in the noncommutative φ 6 model

We study the conditions for spontaneous symmetry breaking of the $(2+1)$-dimensional noncommutative ${\ensuremath{\varphi}}^{6}$ model in the small-\ensuremath{\theta} limit. In this regime, considering the model as a cutoff theory, it is reasonable to assume translational invariance as a property of the vacuum state and study the conditions for spontaneous symmetry breaking by an effective potential analysis. An investigation of up to the two-loop level reveals that noncommutative effects can modify drastically the shape of the effective potential. Under reasonable conditions, the nonplanar sector of the theory can become dominant and induce symmetry breaking for values of the mass and coupling constants not reached by the commutative counterpart.
On markov chain monte carlo methods for nonlinear and non-gaussian state-space models

In this paper, a nonlinear and/or non‐Gaussian smoother utilizing Markov chain Monte Carlo Methods is proposed, where the measurement and transition equations are specified in any general formulation and the error terms in the state‐space model are not necessarily normal. The random draws are directly generated from the smoothing densities. For random number generation, the Metropolis‐Hastings algorithm and the Gibbs sampling technique are utilized. The proposed procedure is very simple and easy for programming, compared with the existing nonlinear and non‐Gaussian smoothing techniques. Moreover, taking several candidates of the proposal density function, we examine precision of the proposed estimator.
Replex: A Scalable, Highly Available Multi-Index Data Store

The need for scalable, high-performance datastores has led to the development of NoSQL databases, which achieve scalability by partitioning data over a single key. However, programmers often need to query data with other keys, which data stores provide by either querying every partition, eliminating the benefits of partitioning, or replicating additional indexes, wasting the benefits of data replication. In this paper, we show there is no need to compromise scalability for functionality. We present Replex, a datastore that enables efficient querying on multiple keys by rethinking data placement during replication. Traditionally, a data store is first globally partitioned, then each partition is replicated identically to multiple nodes. Instead, Replex relies on a novel replication unit, termed replex, which partitions a full copy of the data based on its unique key. Replexes eliminate any additional overhead to maintaining indices, at the cost of increasing recovery complexity. To address this issue, we also introduce hybrid replexes, which enable a rich design space for trading off steady-state performance with faster recovery. We build, parameterize, and evaluate Replex on multiple dimensions and find that Replex surpasses the steady-state and failure recovery performance of Hyper-Dex, a state-of-the-art multi-key data store.
Effective traffic control for military tactical wireless mobile ad-hoc network

The MIL-STD-188-220 is the military tactical wireless mobile ad-hoc communication standard which makes a smooth data and voice communication possible. This standard is known as the Interoperability Standard for Digital Message Transfer Device Subsystems. However, the standard has the problem to generate the meaningless traffic due to the failure of topology update, in the case of transmitting the only packet not requiring the acknowledgments to the destination node away from the radio propagation scope. Moreover, a source requires the intranet end-to-end acknowledgment from all destinations to confirm a packet delivery, regardless of the hop distance to destination nodes. This method has the problem to generate unnecessary traffic in the case to confirm the packet delivery within 1-hop distance. In order to resolve these problems, this paper has proposed the methods that restrain the meaningless traffic generation and simplify the procedure that confirms a packet delivery within 1-hop distance.
TreeDomViewer: a tool for the visualization of phylogeny and protein domain structure

Phylogenetic analysis and examination of protein domains allow accurate genome annotation and are invaluable to study proteins and protein complex evolution. However, two sequences can be homologous without sharing statistically significant amino acid or nucleotide identity, presenting a challenging bioinformatics problem. We present TreeDomViewer, a visualization tool available as a web-based interface that combines phylogenetic tree description, multiple sequence alignment and InterProScan data of sequences and generates a phylogenetic tree projecting the corresponding protein domain information onto the multiple sequence alignment. Thereby it makes use of existing domain prediction tools such as InterProScan. TreeDomViewer adopts an evolutionary perspective on how domain structure of two or more sequences can be aligned and compared, to subsequently infer the function of an unknown homolog. This provides insight into the function assignment of, in terms of amino acid substitution, very divergent but yet closely related family members. Our tool produces an interactive scalar vector graphics image that provides orthological relationship and domain content of proteins of interest at one glance. In addition, PDF, JPEG or PNG formatted output is also provided. These features make TreeDomViewer a valuable addition to the annotation pipeline of unknown genes or gene products. TreeDomViewer is available at .
Lattice generation with accurate word boundary in WFST framework

This paper presents an algorithm to generate the speech recognition lattice with accurate word boundary in weighted finite-state transducer (WFST) decoding framework. In traditional WFST lattice generation algorithms, the transformation from context-dependent phone lattice to word lattice does not yield accurate time boundaries between words. Meanwhile, this lattice is not a Standard Lattice Format nor is it compatible with existing toolkits. The lattice without word boundary can only be used in the area where the word boundary is not needed. In this paper, we propose a lexicon matching algorithm based on token passing to transform the phone lattice to the word lattice. This algorithm generates standard lattices with accurate word boundary. The experiments show that the proposed lattice generation algorithm has an good lattice quality and good algorithm efficiency.
Bayesian Analysis of Empirical Software Engineering Cost Models

Many parametric software estimation models have evolved in the last two decades (L.H. Putnam and W. Myers, 1992; C. Jones, 1997; R.M. Park et al., 1992). Almost all of these parametric models have been empirically calibrated to actual data from completed software projects. The most commonly used technique for empirical calibration has been the popular classical multiple regression approach. As discussed in the paper, the multiple regression approach imposes a few assumptions frequently violated by software engineering datasets. The paper illustrates the problems faced by the multiple regression approach during the calibration of one of the popular software engineering cost models, COCOMO II. It describes the use of a pragmatic 10 percent weighted average approach that was used for the first publicly available calibrated version (S. Chulani et al., 1998). It then moves on to show how a more sophisticated Bayesian approach can be used to alleviate some of the problems faced by multiple regression. It compares and contrasts the two empirical approaches, and concludes that the Bayesian approach was better and more robust than the multiple regression approach.
Development of microgripper system

The paper presents the experimental development of micro/nano robotic cell for assembling the micro objects. Three techniques for gripping of micro objects with micro/nano robot are presented. The first one uses a two finger gripper and is suitable for objects which are greater than 100 μm. The next technique uses the micro tube for development of pneumatic gripper and can be used for objects greater than 70 μm. The third technique is called a one finger gripper, which can be used for objects smaller than 70 μm. The 5 DOF micro/nano robot mechanism with linear step piezoelectric motors and its control system with man-machine communication are also described.
A Guide to Dynamic Load Balancing in Distributed Computer Systems

Summary Load balancing is the process of redistributing the work load among nodes of the distributed system to improve both resource utilization and job response time while also avoiding a situation where some nodes are heavily loaded while others are idle or doing little work. A dynamic load balancing algorithm assumes no a priori knowledge about job behavior or the global state of the system, i.e., load balancing decisions are solely based on the current status of the system. The development of an effective dynamic load balancing algorithm involves many important issues: load estimation, load levels comparison, performance indices, system stability, amount of information exchanged among nodes, job resource requirements estimation, job’s selection for transfer, remote nodes selection, and more. This paper presents and analyses the aforementioned issues that need to be considered in the development or study of a dynamic load balancing algorithm.
Particulate organic matter as a food source for a coral reef sponge

SUMMARY The ability of sponges to feed in diverse (including oligotrophic) ecosystems significantly contributes to their ubiquitous aquatic distribution. It was hypothesized that sponges that harbour small amounts of symbiotic bacteria in their mass feed mainly on particulate organic matter (POM). We examined the nearly symbiont-free (by microscopic observation) filter-feeding Red Sea sponge Negombata magnifica in order to: (a) study removal efficiency of naturally occurring organic particles, (b) measure the total amount of absorbed particulate organic carbon (POC) and nitrogen (PON), and (c) estimate organic carbon and nitrogen flux in this sponge. Total amount of organic carbon and nitrogen in the Gulf of Aqaba was found to be 48.46±5.69 μg l–1 and 6.45±0.7 μg l–1, respectively. While detritus contributed 54% of POC, most PON (84%) came from planktonic microorganisms, mainly prokaryotes. Particle removal efficiency ranged from 99% (the cyanobacterium Synechococcus sp.) to 37% (for eukaryotic cells >8 μm). On average, N. magnifica ingested 480 μg C day–1 g–1 (wet mass, WM) sponge and 76.6 μg N day–1 g–1 sponge. Ingested POC balanced 85% of the sponge's energetic demand but more is needed for biomass production because it cannot digest all of the carbon. 54.4±16.1 μg N day–1 g–1 (WM) nitrogen was excreted as total ammonia nitrogen (TAN); however, nitrogen allowance should be higher because more nitrogen is deposited for sponge biomass during growth. It is hypothesized that the discrepancy in the nutritional requirements should be covered by the sponge absorbing carbon and nitrogen from sources that are not dealt with in the present research, such as dissolved organic carbon and nitrogen. This study highlights the significance of detritus as a carbon source, and prokaryotes as a PON source in sponge feeding.
IMPROVING SUPERVISED CLASSIFICATION OF DAILY ACTIVITIES LIVING USING NEW COST SENSITIVE CRITERION FOR C-SVM

The growing population of elders in the society calls for a new approach in care giving. By inferring what activities elderly are performing in their houses it is possible to determine their physical and cognitive capabilities. In this paper we show the potential of important discriminative classifiers namely the Soft-Support Vector Machines (C-SVM), Conditional Random Fields (CRF) and k-Nearest Neighbors (k-NN) for recognizing activities from sensor patterns in a smart home environment. We address also the class imbalance problem in activity recognition field which has been known to hinder the learning performance of classifiers. Cost sensitive learning is attractive under most imbalanced circumstances, but it is difficult to determine the precise misclassification costs in practice. We introduce a new criterion for selecting the suitable cost parameter C of the C-SVM method. Through our evaluation on four real world imbalanced activity datasets, we demonstrate that C-SVM based on our proposed criterion outperforms the state-of-the-art discriminative methods in activity recognition.
Multi-task Multiple Kernel Learning

This paper presents two novel formulations for learning shared feature representations across multiple tasks. The idea is to pose the problem as that of learning a shared kernel, which is constructed from a given set of base kernels, leading to improved generalization in all the tasks. The first formulation employs a (l1, lp), p ≥ 2 mixed norm regularizer promoting sparse combinations of the base kernels and unequal weightings across tasks — enabling the formulation to work with unequally reliable tasks. While this convex formulation can be solved using a suitable mirror-descent algorithm, it may not learn shared feature representations which are sparse. The second formulation extends these ideas for learning sparse feature representations constructed from multiple base kernels and shared across multiple tasks. The sparse feature representation learnt by this formulation is essentially a direct product of low-dimensional subspaces lying in the induced feature spaces of few base kernels. The formulation is posed as a (l1, lq), q ≥ 1 mixed Schattennorm regularized problem. One main contribution of this paper is a novel mirror-descent based algorithm for solving this problem which is not a standard set-up studied in the optimization literature. The proposed formulations can also be understood as generalizations of the framework of multiple kernel learning to the case of multiple tasks and hence are suitable for various learning applications. Simulation results on real-world datasets show that the proposed formulations generalize better than state-of-theart. The results also illustrate the efficacy of the proposed mirror-descent based algorithms.
A Comparative Study of Five Regression Testing Techniques : A Survey

Regression testing is a style of testing that focuses on retesting after changes are made. In traditional regression testing, we reuse the same tests (the regression tests). Over the years, numerous regression test optimization techniques have been described in the literature. Empirical studies of the techniques suggest that they can indeed benefit testers, but so far, few studies have empirically compared different techniques. In this paper, the results of a comparative empirical study of five different regression test optimization techniques are represented. The comparison is based on different qualitative and quantitative criteria such as number of tests selected, execution time of tests, precision, user parameters, global variables handling, type of testing etc. These algorithms are found to be suitable for different requirements of regression testing. General Terms: Regression Testing Optimization Techniques
Molecular cloning of chick beta-tectorin, an extracellular matrix molecule of the inner ear

The tectorial membrane is an extracellular matrix lying over the apical surface of the auditory epithelium. Immunofluorescence studies have suggested that some proteins of the avian tectorial membrane, the tectorins, may be unique to the inner ear (Killick, R., C. Malenczak, and G. P. Richardson. 1992. Hearing Res. 64:21-38). The cDNA and deduced amino acid sequences for chick beta-tectorin are presented. The cDNA encodes a protein of 36,902.6 D with a putative signal sequence, four potential N-glycosylation sites, 13 cysteines, and a hydrophobic COOH terminus. Western blots of two-dimensional gels using antibodies to a synthetic peptide confirm the identity of the cDNA. Southern and Northern analysis suggests that beta-tectorin is a single-copy gene only expressed in the inner ear. The predicted COOH terminus is similar to that of glycosylphosphatidylinositol-linked proteins, and antisera raised to this region react with in vitro translation products of the cDNA clone but not with mature beta-tectorin. These data suggest beta- tectorin is synthesized as a glycosylphosphatidyl-inositol-linked precursor, targeted to the apical surface of the sensory epithelium by the lipid moiety, and then further processed. Sequence analysis indicates the predicted protein possesses a zona pellucida domain, a sequence that is common to a limited number of other matrix-forming proteins and may be involved in the formation of filaments. In the cochlear duct, beta-tectorin is expressed in the basilar papilla, in the clear cells and the cuboidal cells, as well as in the striolar region of the lagena macula. The expression of beta-tectorin is associated with hair cells that have an apical cell surface specialization known as the 275-kD hair cell antigen restricted to the basal region of the hair bundle, suggesting that matrices containing beta-tectorin are required to drive this hair cell type.
Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry studies

We discuss the evaluation of subsets of variables for the discriminative evidence they provide in multivariate mixture modeling for classification. The novel development of Bayesian classification analysis presented is partly motivated by problems of design and selection of variables in biomolecular studies, particularly involving widely used assays of large-scale single-cell data generated using flow cytometry technology. For such studies and for mixture modeling generally, we define discriminative analysis that overlays fitted mixture models using a natural measure of concordance between mixture component densities, and define an effective and computationally feasible method for assessing and prioritizing subsets of variables according to their roles in discrimination of one or more mixture components. We relate the new discriminative information measures to Bayesian classification probabilities and error rates, and exemplify their use in Bayesian analysis of Dirichlet process mixture models fitted via Markov chain Monte Carlo methods as well as using a novel Bayesian expectation–maximization algorithm. We present a series of theoretical and simulated data examples to fix concepts and exhibit the utility of the approach, and compare with prior approaches. We demonstrate application in the context of automatic classification and discriminative variable selection in high-throughput systems biology using large flow cytometry datasets.
Narrative Processing Predicts Well-Being and Communion in Victims of Intimate Partner Violence

Narratives may play an important role in understanding and healing from traumas among victims of intimate partner violence. This study investigated whether narrative processes of overcoming and integrating traumas into overall life stories (i.e., narrative resolution, complexity, and growth) relate to female victims' overall psychological well-being and capacities for close, meaningful relationships following intimate partner violence (N=32). To assess predictor and outcome variables, a narrative protocol and questionnaires examining participants' well-being, relationship attitudes, and relationship engagement were used. Results showed that victims' levels of narrative processing were generally predictive of their intimate relationship attitudes, positive relations with others, and overall well-being. Decreased rumination partially mediated the relationship between narrative resolution and well being. Overall, findings indicate the importance of narratives in trauma recovery in the case of intimate partner violence. Storytelling is pertinent to an individual's self-insight and development both in the therapy setting and in life overall. Storytelling, or narrative processing, is the narration, interpretation, and incorporation of past experiences into individuals' life stories (Pals, 2006). Consequently, narrative processing is important in making meaning of past events, particularly traumatic experiences, within the context of the life story as a whole. People's stories vary in their inclusion of narrative components, such as complexity, resolution, and growth. Overall, the sharing of one's experiences is linked to identity development, well-being, positive relationship development, and meaning in one's life (e.g., King, 2001; Linley & Joseph, 2003). Past research has shaped the current study's focus on a particular trauma in women, Intimate Partner Violence (IPV), and its relationship to narrative processing, which has the potential to be a powerful coping mechanism in trauma victims. Understanding coping in IPV victims is essential, as being able to identify successful coping mechanisms has positive implications for recovery and therapy for victims. The present study centers on one main question: What narrative processes (i.e., complexity, resolution, and growth) predict IPV victims' well-being and capacities for close relationships? Life stories are complex, evolving, psychosocial constructions (i.e., both individuals and their cultures author and shape these stories) associated with identity development (McAdams & Bowman, 2001). Individuals constantly revise their unique life stories to comply with cultural expectations and to incorporate past, present, and future events into these stories 46 experiences, and differences in life experiences are pertinent to identity development within a culture (McLean, 2008). Essentially, identity is an internalized, evolving life story (McAdams, 2001). Past research has shown the …
A Novel Approach for Scenario-Based Test Case Generation

Testing of software is a time-consuming activity which requires a great deal of planning and resources. Model-based testing is gaining importance as a research issue. In scenario-based testing, test scenarios are used for generating test cases, test drivers etc. UML is widely used to describe analysis and design specifications of software development. UML models are important source of information for test case design. UML activity diagrams describe the realization of the operation in design phase and also support description of parallel activities and synchronization aspects involved in different activities perfectly. In this paper we generate test scenarios from activity diagrams, which achieve test adequacy criteria perfectly. Finally we generate test cases by analyzing the respective sequence and class diagrams of each scenario, which achieves maximum path coverage criteria. Also in our approach, the cost of test model creation is reduced as design is reused.