Miracle-dz/UniFAR-FTUs huggingface dataset:
On Resiliency to Compromised Nodes : A Case for Location Based Security in Sensor Networks

Node compromise poses severe security threats in sensor networks. Unfortunately, existing security designs can address only a small, fixed threshold of compromised nodes; the security protection completely breaks down when the threshold is exceeded. This paper studies how to achieve resiliency against an increasing number of compromised nodes. To this end, we propose a novel location-based approach where the secret keys are bound to geographic locations, and each node stores a few keys based on its own location. The location-binding property constrains the purposes for which individual keys can be used, and decouples the keys bound to different locations, thus limiting the damages caused by a group of compromised nodes. We illustrate this approach through the problem of report fabrication attacks, where compromised nodes forge non-existent events. We evaluate our approach through extensive analysis, implementation, and simulations, and demonstrate its graceful performance degradation when more and more nodes are compromised.
Applying an Organizational Uncertainty Principle: Semantic Web-Based Metrics

The theory of bistable perceptions in the interaction indicates the existence of an uncertainty principle with effects amplified at the organizational level. Traditional theory of the interaction, organizational theory, and the justification for an organizational uncertainty principle are reviewed. The organizational uncertainty principle predicts counterintuitive effects that can be exploited with the Semantic Web to formulate a set of metrics for organizational performance. As a preliminary test of the principle, metrics derived from it are applied to two case studies, both works in progress, with the first as an ongoing large system-wide application of web-based metrics for organizational performance and the second as a case study of a small college where web-based metrics are being considered and constructed. In preparation for the possibility of machine-based real-time metrics afforded by the Semantic Web, the results demonstrate a successful theory and application in the field of an uncertainty principle for organizations.
A sub-linear time distributed algorithm for minimum-weight spanning trees

This paper considers the question of identifying the parameters governing the behavior of fundamental global network problems. Many papers on distributed network algorithms consider the task of optimizing the running time successful when an O(n) bound is achieved on an n-vertex network. We propose that a more sensitive parameter is the network's diameter Diam. This is demonstrated in the paper by providing a distributed minimum-weight spanning tree algorithm whose time complexity is sub-linear in n, but linear in Diam (specifically, O(Diam+n/sup 0.614/)). Our result is achieved through the application of graph decomposition and edge elimination techniques that may be of independent interest. <<ETX>>
Estimation of sensor-network topology from time-series sensor data using ant colony optimization method

We propose a method for estimating sensor network topology from only with time-series sensor data and without prior knowledge about the locations of sensors. The proposed method is based on ant colony optimization (ACO) but is further improved, compared with previous work[s], to construct a more accurate topology through an examination of the reliability of the acquired sensor data for the adjacency estimation. This reliability value is used to control the amount of pheromones deposited. We evaluate our method using actual sensor data and show that it can estimate adjacencies, in which the error rate is approximately 87% less than that of the previous method.
Tuberculosis Infection in Cattle and Cattle Owners in North Eastern Parts of Ethiopia

Introduction: Ethiopia is one of the highest TB burden countries in the world. Transmission of M. tuberculosis between humans is well known, however, little is known about the transmission of M. tuberculosis complex species between human and animals, in particular cattle. The purpose of this study was, therefore, to investigate the status of bovine tuberculosis infection in cattle owned by tuberculosis suspected households and isolate M. tuberculosis complex species from sputum of chronic coughers in North Wollo Zone of the Amhara National Regional State, Ethiopia. Method: A cross-sectional study was conducted in selected Kebeles of North Wollo Zone. An intradermal tuberculin skin test was carried out on 381 cattle and sputum samples collected from 124 chronic coughers. Sputum samples were cultured and positive ones were identified by polymerase chain reaction using RD4 and RD9 as a marker. Result: Of the 124 sputum samples cultured, 4/ 124 (3.2%) were positive. Polymerase Chain Reaction using has confirmed that 2/4 (50%) were found to be M. tuberculosis while the rest was atypical Mycobacterial species. Of the 381 tested cattle 5/381 (1.31%) and 10/38 (2.63%) were found positive according to manufacturer recommendation (with a cutoff value > 4 mms) and a cutoff value > 2 mms, respectively. About 67% (6/9) of the individuals who owned positive cattle were having the habit of drinking raw milk. Among the ten positives, five of them resided in lowland (Kolla) areas (1300-1500 mabs). However, none of the owners of tuberculin positive cattle were found to be TB positive. Conclusion: This study has shown 3.2% of culture positivity and of these two were found to be M. tuberculosis. The sputum sample culturing from suspected chronically coughing individuals would help detection of the TB causative agents. According to the current findings BTB is considerable in Ethiopia and cattle should be seen as a potential source of zoonotic TB to humans
InvMixColumn decomposition and multilevel resource sharing in AES implementations

Hardware implementations of cryptography face increasingly more stringent demands for lower cost and greater computational power. In order to meet those demands, more efficient approaches to implementations are needed. This paper presents detailed studies of MixColumn and InvMixColumn operations used in Advanced Encryption Standard that aim at their hardware implementations in constrained environments. Our studies are supported by mathematical analysis of both transformations and lead to efficient serial and parallel decompositions. Furthermore, deeper resource sharing is demonstrated at word-, byte- and bit-level. All derived architectures are evaluated using popular low-cost field-programmable gate arrays. Application of proposed methods resulted in reduction of reconfigurable logic area of the complete cipher by up to 20%.
SAP2P: Self-adaptive and Locality-aware P2P Membership Protocol for Heterogeneous Systems

Membership protocols are the basic utilities for P2P multicast applications. Existing membership protocols always assume a homogeneous environment or bring non-negligible overhead to optimize the overlay topology. In this paper, we propose SAP2P, a self-adaptive and locality-aware P2P membership protocol for heterogeneous systems. SAP2P periodically optimizes the overlay in terms of node degree and link latency. In SAP2P, the degrees of member nodes are proportional to their capacities and neighbors are always physically close. To reduce the control overhead, SAP2P uses landmark based scheme to evaluate link latencies and takes advantage of a Markov chain based method to adoptively vary the optimization periods. Simulation study shows that SAP2P can achieve good qualities in terms of load balancing and locality awareness with relatively smaller overhead, while it still responds quickly to node churn. Specially, compared with randomized membership protocols, SAP2P achieves up to 53.7% latency reduction.
Interactive machine learning: letting users build classifiers

Abstract According to standard procedure, building a classifier using machine learning is a fully automated process that follows the preparation of training data by a domain expert. In contrast, interactive machine learning engages users in actually generating the classifier themselves. This offers a natural way of integrating background knowledge into the modelling stage—as long as interactive tools can be designed that support efficient and effective communication. This paper shows that appropriate techniques can empower users to create models that compete with classifiers built by state-of-the-art learning algorithms. It demonstrates that users—even users who are not domain experts—can often construct good classifiers, without any help from a learning algorithm, using a simple two-dimensional visual interface. Experiments on real data demonstrate that, not surprisingly, success hinges on the domain: if a few attributes can support good predictions, users generate accurate classifiers, whereas domains with many high-order attribute interactions favour standard machine learning techniques. We also present an artificial example where domain knowledge allows an “expert user” to create a much more accurate model than automatic learning algorithms. These results indicate that our system has the potential to produce highly accurate classifiers in the hands of a domain expert who has a strong interest in the domain and therefore some insights into how to partition the data. Moreover, small expert-defined models offer the additional advantage that they will generally be more intelligible than those generated by automatic techniques.
Fluctuating fire regimes and their historical effects on genetic variation in an endangered shrubland specialist

Abstract The Pleistocene was characterized by worldwide shifts in community compositions. Some of these shifts were a result of changes in fire regimes, which influenced the distribution of species belonging to fire‐dependent communities. We studied an endangered juniper–oak shrubland specialist, the black‐capped vireo (Vireo atricapilla). This species was locally extirpated in parts of Texas and Oklahoma by the end of the 1980s as a result of habitat change and loss, predation, brood parasitism, and anthropogenic fire suppression. We sequenced multiple nuclear loci and used coalescence methods to obtain a deeper understanding of historical population trends than that typically available from microsatellites or mtDNA. We compared our estimated population history, a long‐term history of the fire regime and ecological niche models representing the mid‐Holocene, last glacial maximum, and last interglacial. Our Bayesian skyline plots showed a pattern of historical population fluctuation that was consistent with changing fire regimes. Genetic data suggest that the species is genetically unstructured, and that the current population should be orders of magnitude larger than it is at present. We suggest that fire suppression and habitat loss are primary factors contributing to the recent decline of the BCVI, although the role of climate change since the last glacial maximum is unclear at present.
Cost analysis of nurse telephone consultation in out of hours primary care: evidence from a randomised controlled trial

Abstract Objective: To undertake an economic evaluation of nurse telephone consultation using decision support software in comparison with usual general practice care provided by a general practice cooperative. Design: Cost analysis from an NHS perspective using stochastic data from a randomised controlled trial. Setting: General practice cooperative with 55 general practitioners serving 97 000 registered patients in Wiltshire, England. Subjects: All patients contacting the service, or about whom the service was contacted during the trial year (January 1997 to January 1998). Main outcome measures: Costs and savings to the NHS during the trial year. Results: The cost of providing nurse telephone consultation was £81 237 per annum. This, however, determined a £94 422 reduction of other costs for the NHS arising from reduced emergency admissions to hospital. Using point estimates for savings, the cost analysis, combined with the analysis of outcomes, showed a dominance situation for the intervention over general practice cooperative care alone. If a larger improvement in outcomes is assumed (upper 95% confidence limit) NHS savings increase to £123 824 per annum. Savings of only £3728 would, however, arise in a scenario where lower 95% confidence limits for outcome differences were observed. To break even, the intervention would have needed to save 138 emergency hospital admissions per year, around 90% of the effect achieved in the trial. Additional savings of £16 928 for general practice arose from reduced travel to visit patients at home and fewer surgery appointments within three days of a call. Conclusions: Nurse telephone consultation in out of hours primary care may reduce NHS costs in the long term by reducing demand for emergency admission to hospital. General practitioners currently bear most of the cost of nurse telephone consultation and benefit least from the savings associated with it. This indicates that the service produces benefits in terms of service quality, which are beyond the reach of this cost analysis.
Acute Treatment with a Novel TRPC4/C5 Channel Inhibitor Produces Antidepressant and Anxiolytic-Like Effects in Mice

Transient receptor potential canonical (TRPC) channels are widely expressed in brain and involved in various aspects of brain function. Both TRPC4 and TRPC5 have been implicated in innate fear function, which represents a key response to environmental stress. However, to what extent the TRPC4/C5 channels are involved in psychiatric disorders remains unexplored. Here, we tested the antidepressant and anxiolytic-like effects of a newly identified TRPC4/C5 inhibitor, M084. We show that a single intraperitoneal administration of M084 at 10 mg/kg body weight to C57BL/6 male mice significantly shortened the immobility time in forced swim test and tail suspension test within as short as 2 hours. The M084-treated mice spent more time exploring in illuminated and open areas in light/dark transition test and elevated plus maze test. In mice subjected to chronic unpredictable stress, M084 treatment reversed the enhanced immobility time in forced swim test and decreased the latency to feed in novelty suppressed feeding test. The treatment of M084 increased BDNF expression in both mRNA and protein levels, as well as phosphorylation levels of AKT and ERK, in prefrontal cortex. Our results indicate that M084 exerts rapid antidepressant and anxiolytic-like effects at least in part by acting on BDNF and its downstream signaling. We propose M084 as a lead compound for further druggability research.
A Core Ontology for Spatial Reasoning

In this paper we describe a core ontology for N-dimensional spatial reasoning. The ontology is intended to support both quantitative and qualitative approaches and is expressed using set notation. Using the ontology; spatial domains of discourse, spatial objects and their attributes, and the relationships that can link spatial objects can be expressed in terms of sets, and sets of sets. The core ontology has been emphparameterised to express a number of task dependent ontologies in application areas such as Geographic Information Systems (GIS), noise pollution monitoring, environmental impact assessment, shape tting, timetabling and scheduling, and AI problems such as the N-queens problem. We illustrate this parameterisation by using the core ontology to express both Allen's interval calculus, and Egenhofer's \9-Intersection" approach to topological spatial reasoning. Although there is still much work to do, the directed task ontologies that have been investigated indicate that the core ontology described provides an excellent foundation for further research.
Degraded Acceptability And Markedness in Syntax And The Stochastic Interpretation Of Optimality Theory

Conceiving grammaticality as gradient poses problems for those traditional conceptions of grammar which assume that linguistic expressions can only be either grammatical or ungrammatical. That a sentence is, for instance, “grammatical to 75%” is a nonsensical statement from this point of view. In this tradition, generative grammar assumes the native speaker’s linguistic competence to be the system of rules with which, among other things, she classifies sentences as either grammatical or ungrammatical. A linguistic theory is explanatorily adequate to the extent that it successfully models this underlying knowledge. As a consequence of the categorical view on grammaticality, generative models rarely reflect gradience. The minimalist program (Chomsky, 1995) for generative syntax knows “converging” and “crashing” derivations, which conform to grammatical and ungrammatical structures. But
Homogenization of semilinear parabolic equations in domains with spherical traps

The semilinear parabolic problem in a domain in Rn with a large number of periodically located and perforated spheres is considered. It is assumed that the period of the structure is e and that the radius of the perforations is of order en/n-2 . The homogenization of this problem leads to a system of a non linear parabolic p.d. e. coupled with a non linear o.d. e.
An Evolutionary Computational Approach for Optimizing Broadcasting in Disaster Response Scenarios

Rescue teams require exchanging tactical information to efficiently adapt their behavior to the particular disaster response scenario conditions. Broadcasting is the main technique used to disseminate messages throughout nodes in ad hoc networks. Ad hoc networks are one of the most appealing networks to be used in disaster response scenario due to their decentralized and self-organizing features. While many broadcasting techniques have been presented for ad hoc networks, there are not adaptive broadcasting schemes for disaster response scenarios so far. The complexity of designing broadcasting protocols for disaster response scenarios is high due to the large number of factors affecting the communications among nodes, such as mobility, density, message collisions, etc. In this paper we propose an evolutionary computational approach for designing and optimizing a probability-based broadcast scheme using topological features of nodes. The validity of the proposed approach is shown through simulations.
Residual energy-aware collaborative transmission beamforming in wireless sensor networks

Energy-Efficient transmission techniques are very important for extending the lifetime of a wireless sensor network (WSN) given that recharging batteries of a large number of WSN nodes is a very difficult and expensive operation. Collaborative transmission beamforming (CTB) saves energy consumed by each node by distributing between different WSN nodes the required total power transmission to get a desired bit error rate (BER) at the receiver. Moreover, by coherently combining the different signals transmitted by the WSN nodes, CTB increases the signal strength in the direction of the receiver, therefore decreases the total power transmission of the WSN. In this paper, we propose a new CTB technique that minimizes the total power transmission of the WSN while balancing the residual energy in different nodes. By solving this multi-objective optimization problem, we show that the WSN lifetime can be improved up to 30 % compared to the basic CTB algorithm, which aims at minimizing the total power transmission without taking into account the residual energy.
An object model for a dynamic mixin based language

Dynamic mixins allow objects to be modified at runtime with modular extensions. Runtime extensions complicate the object memory management because the order in which mixins are added is not known at compile time. Furthermore, the exact type of the receiver of an up-call from a mixin must be determined at runtime. This work proposes an object model used to address both of these issues. It is used to implement a dynamic mixin-based language called mix and removes the burden of object memory management from the application developer.
Prion Filament Networks in [Ure3] Cells of Saccharomyces cerevisiae

The [URE3] prion (infectious protein) of yeast is a self-propagating, altered form of Ure2p that cannot carry out its normal function in nitrogen regulation. Previous data have shown that Ure2p can form protease-resistant amyloid filaments in vitro, and that it is aggregated in cells carrying the [URE3] prion. Here we show by electron microscopy that [URE3] cells overexpressing Ure2p contain distinctive, filamentous networks in their cytoplasm, and demonstrate by immunolabeling that these networks contain Ure2p. In contrast, overexpressing wild-type cells show a variety of Ure2p distributions: usually, the protein is dispersed sparsely throughout the cytoplasm, although occasionally it is found in multiple small, focal aggregates. However, these distributions do not resemble the single, large networks seen in [URE3] cells, nor do the control cells exhibit cytoplasmic filaments. In [URE3] cell extracts, Ure2p is present in aggregates that are only partially solubilized by boiling in SDS and urea. In these aggregates, the NH2-terminal prion domain is inaccessible to antibodies, whereas the COOH-terminal nitrogen regulation domain is accessible. This finding is consistent with the proposal that the prion domains stack to form the filament backbone, which is surrounded by the COOH-terminal domains. These observations support and further specify the concept of the [URE3] prion as a self-propagating amyloid.
Direct Maximization of Rank-based Metrics * * DRAFT IN PROGRESS * *

The ability to rank instances is important for a number of applications, such as information retrieval and collaborative filtering. It is often the case that the underlying task attempts to maximize some evaluation metric, such as mean average precision, over rankings. Most past work on learning how to rank has focused on likelihoodor margin-based approaches. In this work we explore directly maximizing rank-based metrics, which are a family of metrics that only depend on the order of ranked items. This allows us to maximize different metrics for the same training data. We show how the parameter space of linear (and monotonically linear) scoring functions can be reduced to a multinomial manifold. Parameter estimation is accomplished by solving an optimization procedure that directly maximizes the evaluation metric over the manifold. Results from ad hoc information retrieval are given that show significant improvements in effectiveness using the proposed model.