Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Byzantine Tolerance for Cloud Computing

Tolerating Byzantine failures in the context of cloud computing is costly. Traditional BFT protocols induce a fixed degree of replication for computations and are therefore wasteful. This paper explores probabilistic Byzantine tolerance, in which computation tasks are replicated on dynamic replication sets whose size is determined based on ensuring probabilistic thresholds of correctness. The probabilistic assessment of a trustworthy output by selecting reputable nodes allows a significant reduction in the number of nodes involved in each computation task. The paper further studies several reputation management policies, including the one used by BOINC as well as a couple of novel ones, in terms of their impact of the possible damage inflicted on the system by various Byzantine behavior strategies, and reports some encouraging insights.
A MOBILE, MAP-BASED TASKING INTERFACE FOR HUMAN-ROBOT INTERACTION By

whom graciously volunteered to serve on my thesis committee. Both professors were approachable, understanding , and more than willing to take time out of their busy schedules to accommodate my needs. For these reasons, they were invaluable to my success and I am grateful for their participation and involvement. This thesis would not have been possible without the hard work of two dedicated individuals within the individuals were pivotal in laying the initial groundwork for the map-based tasking interface described in this work. In the case of Mr. Hayes, the reader will find footnotes throughout this work acknowledging his specific contributions. Mr. Hayes also graciously offered his time to discuss the intricacies of the statistical methods used in this thesis. Mr. Freedman continues to lend his assistance to my research to this day, frequently providing a discerning eye that has been invaluable to improving the research presented in this thesis.
Using Outlier Detection to Reduce False Positives in Intrusion Detection

Intrusion detection systems (IDSs) can easily create thousands of alerts per day, up to 99% of which are false positives (i.e. alerts that are triggered incorrectly by benign events). This makes it extremely difficult for managers to analyze and react to attacks. This paper presents a novel method for handling IDS alerts more efficiently. It introduces outlier detection technique into this field, and designs a special outlier detection algorithm for identifying true alerts and reducing false positives. This algorithm uses frequent attribute values mined from historical alerts as the features of false positives, and then filters false alerts by the score calculated based on these features. We also proposed a two-phrase framework, which not only can filter newcome alerts in real time, but also can learn from these alerts and automatically adjust the filtering mechanism to new situations. Moreover our method needs no domain knowledge and little human assistance, so it is more practical than current ways. We have built a prototype implementation of our method. And the experiments on DARPA 2000 and real-world data have proved that this model has high performance.
THE BOLOCAM GALACTIC PLANE SURVEY. VII. CHARACTERIZING THE PROPERTIES OF MASSIVE STAR-FORMING REGIONS

We present the results of a Green Bank Telescope survey of NH3(1,1), (2,2), (3,3) lines toward 631 Bolocam Galactic Plane Survey (BGPS) sources at a range of Galactic longitudes in the inner Galaxy. We have detected the NH3(1,1) line toward 72% of our targets (456), demonstrating that the high column density features identified in the BGPS and other continuum surveys accurately predict the presence of dense gas. We have determined kinematic distances and resolved the distance ambiguity for all BGPS sources detected in NH3. The BGPS sources trace the locations of the Scutum and Sagittarius spiral arms, with the number of sources peaking between RGal ∼ 4 and 5 kpc. We measure the physical properties of each source and find that depending on the distance, BGPS sources are primarily clumps, with some cores and clouds. We have examined the physical properties as a function of Galactocentric distance, and find a mean gas kinetic temperature of 15.6 K, and that the NH3 column density and abundance decrease by nearly an order of magnitude between RGal ∼ 3 and 11 kpc. Comparing sources at similar distances demonstrates that the physical properties are indistinguishable, which suggests a similarity in clump structure across the Galactic disk. We have also compared the BGPS sources to criteria for efficient star formation presented independently by Heiderman et al. and Lada et al., and for massive star formation presented by Kauffmann et al. Forty-eight percent of our sample should be forming stars (including massive stars) with high efficiency, and 87% contain subregions that should be efficiently forming stars. Indeed, we find that 67% of the sample exhibit signs of star formation activity based on an association with a mid-infrared source.
DG 2 CEP : A Density-Grid Stream Clustering Algorithm based on Complex Event Processing for Cluster Detection

Applications such as fleet and mobile task force management, or traffic control can largely benefit from the on-line detection of collective mobility patterns of vehicles, goods or persons. A common mobility pattern is a cluster, a concentration of mobile nodes in a certain region, e.g., a mass protest, a rock concert, or a traffic jam. Current approaches require previous knowledge of the locations where the cluster might happen. In this paper, we propose DG2CEP, an algorithm inspired by data mining algorithms and based on Complex Event Processing, for on-line detection of such clusters. It can detect the formation and dispersion of clusters from streams of position data without the need of specifying the possible locations of clusters in advance.
Transactions and paths: Two use case based metrics which improve the early effort estimation

The fact that Albrecht's function point method must be frequently improved or adjusted to suit new development contexts shows that there is a need for a new approach to early estimate effort. Such approach should reduce the estimation error and it is convenient for it to be based on use cases. The authors defined two simple metrics which capture two key aspects of software applications, size and complexity, which are called Transactions (T) and Paths (P). Also, in order to demonstrate that it is possible to reduce the error of effort estimation using these two new metrics, a case study was developed.
Decision making and partner selection based on trust in multi-context environment

Currently there are exist many different trust models for different purposes. These models are often used for collaborative filtering to find partner for interaction in distributed systems. Most of these models simple uses decision making and partner selection approaches based on highest trust level selection method to choose an entity for an interaction. We assume that decision making and selection most reliable partner based on trust require more complex solutions because it is key aspect when evaluating an efficient trust model. In this article we compares two different kind of partner selection methods based on highest trust level selection and the roulette wheel selection.
‘A critical autoethnography of a doctoral students’ research journey: learning to take risks in the academy’

ABSTRACT What is missing in present-day physical education teacher education research is the individual female doctoral student perspective and how individuals come to understand academic research culture within the neoliberal university. Through a critical autoethnography, this paper uncovered a transformative learning journey of one doctoral student as she encountered the field of research in higher education. After taking a critically orientated qualitative methods class, the doctoral student recognised that the neoliberal university includes a research agenda entwined in politics, finding that neophyte researchers should be aware of the ‘mess’ (Cheek, J. (2017). Qualitative inquiry, research marketplaces, and neoliberalism. In N. K. Denzin, & M. D. Giardina (Eds.), Qualitative Inquiry in Neoliberal Times (pp. 19–36). New York: Routledge). By questioning how one is disciplined in research and through becoming aware of normalising techniques, the doctoral student interrogated her research methods and philosophical orientation. Ethnodrama (Denzin, N. K. (2010). The qualitative manifesto. Walnut Creek: Left Coast Press) and autoethnography provided the doctoral student with an opportunity for alternate meaning-making, which can be productive in understanding the journey of becoming in academia.
Space Mapping Optimization Algorithms for Engineering Design

A simple, efficient optimization algorithm based on space mapping (SM) is presented. It utilizes input SM to reduce the misalignment between the coarse and fine models of the optimized object over a region of interest, and output space mapping (OSM) to ensure matching of response and first-order derivatives between the mapped coarse model and the fine model at the current iteration point. We also consider an enhanced version in which the input SM coefficients are frequency dependent. The performance of our new algorithms is comparable with the recently published SMIS algorithm when applied to a benchmark problem. In comparison with SMIS, the models presented are simple and have a small number of parameters that need to be extracted. The new algorithm is applied to the optimization of a coupled-line band-pass filter
The Circumstellar Environment of the Early B Protostar G192.16-3.84 and the Discovery of a Low-Mass, Protostellar Core

We have observed the massive star-forming region associated with the early B protostar G192.16-3.84 in NH3(1, 1), 22.2 GHz H2O masers, 1.3 cm continuum emission, and at 850 μm. The dense gas associated with G192.16 is clumpy, is optically thin, and has a mass of 0.9 M☉. The NH3 core is gravitationally unstable, which may signal that the outflow phase of this system is coming to an end. Water masers trace an ionized jet 0. ″8 (1600 AU at a distance of 2 kpc) north of G192.16. Masers are also located within 500 AU of G192.16; their velocity distribution is consistent with but does not strongly support the interpretation that the maser emission arises in a 1000 AU rotating disk centered on G192.16. Roughly 30'' south of G192.16 (0.3 pc) is a compact, optically thick (τ = 1.2) NH3 core (called G192 S3) with an estimated mass of 2.6 M☉. Based on the presence of 850 μm and 1.2 mm continuum emission, G192 S3 probably harbors a very young, low-mass protostar or protocluster. The dense gas in the G192 S3 core is likely to be gravitationally bound and may represent the next site of star formation in this region.
A regional labour market model for analyzing the impact of a recession

The effects of the recent economic recession have been widely discussed, particularly at the macro economic level. However, the economic downturn has been pervasive and has also determined a range of economic effects at different territorial levels. It has therefore become necessary to set up appropriate analytical tools aimed at investigating the impact of the economic downturn at the regional level, and to implement adequate policy options to mitigate such negative impacts. We propose a new macro-micro econometric framework which incorporates simultaneously both aggregate labour demand and supply, and the labour market flows determining the steady-state unemployment rate. We can thus simulate demand or supply shocks and therefore assess their impacts on labour demand and supply, and also on unemployment and labour market flows. This enables us to pinpoint the dynamic effects of such shocks and to compare the different behaviours of the regional framework and of the economy as a whole.
Intercultural sensitivity training toolkit: Augmenting academic engagement patterns

This research paper was reviewed using a double blind peer review process that meets DIISR requirements. Two reviewers were appointed on the basis of their independence and they reviewed the full paper devoid of the authors’ names and institutions in order to ensure objectivity and anonymity. Papers were reviewed according to specified criteria, including relevance to the conference theme and sub-themes, originality, quality and presentation. Following review and acceptance, this full paper was presented at the international conference.
MassChroQ: A versatile tool for mass spectrometry quantification

Recently, many software tools have been developed to perform quantification in LC‐MS analyses. However, most of them are specific to either a quantification strategy (e.g. label‐free or isotopic labelling) or a mass‐spectrometry system (e.g. high or low resolution). In this context, we have developed MassChroQ (Mass Chromatogram Quantification), a versatile software that performs LC‐MS data alignment and peptide quantification by peak area integration on extracted ion chromatograms. MassChroQ is suitable for quantification with or without labelling and is not limited to high‐resolution systems. Peptides of interest (for example all the identified peptides) can be determined automatically, or manually by providing targeted m/z and retention time values. It can handle large experiments that include protein or peptide fractionation (as SDS‐PAGE, 2‐D LC). It is fully configurable. Every processing step is traceable, the produced data are in open standard formats and its modularity allows easy integration into proteomic pipelines. The output results are ready for use in statistical analyses. Evaluation of MassChroQ on complex label‐free data obtained from low and high‐resolution mass spectrometers showed low CVs for technical reproducibility (1.4%) and high coefficients of correlation to protein quantity (0.98). MassChroQ is freely available under the GNU General Public Licence v3.0 at http://pappso.inra. fr/bioinfo/masschroq/.
Intrusion Detection System with Data Stream Modeling using Conditional Privileges

IDS for computer network is capable of detecting and alerting the systems administrator on potential intrusion, providing guidance against any potential loss of integrity and confidentiality to the enterprise’s valuable intellectual assets. In this paper, the layered model for IDS and alert aggregation technique is used. In this layered IDS architecture, each layer assesses, filters, and/or aggregates information produced by a lower layer. Thus, relevant information gets more and more condensed and certain, and, therefore, also more valuable. Alert may originate from low-level IDS such as those mentioned above, from firewalls (FW), etc. Alerts that belong to one attack instance must be clustered together and meta-alerts must be generated. The main goal is to improve performance by reducing the amount of alerts substantially without losing any important information which is necessary to identify ongoing attack instances.
A Scaling Model for Estimating Time-Series Party Positions from Texts

However, existing text-based methods face challenges in producing valid and reliable time-series data. This article proposes a scaling algorithm called WORDFISH to estimate policy positions based on word frequencies in texts. The technique allows researchers to locate parties in one or multiple elections. We demonstrate the algorithm by estimating the positions of German political parties from 1990 to 2005 using word frequencies in party manifestos. The extracted positions reflect changes in the party system more accurately than existing time-series estimates. In addition, the method allows researchers to examine which words are important for placing parties on the left and on the right. We find that words with strong political connotations are the best discriminators between parties. Finally, a series of robustness checks demonstrate that the estimated positions are insensitive to distributional assumptions and document selection.
Implementing OMB's Program Assessment Rating Tool (PART): Meeting the Challenges of Integrating Budget and Performance

This article examines the PART initiative of the United States Office of Management and Budget from a practical standpoint: how federal agencies have dealt with the requirements of PART and what strategies they have employed to be successful. The article highlights four challenges that confront both agencies and OMB as they work to complete assessments of all 1 000 programmes and describes approaches that agencies are taking to meet these challenges.
Improvement of bacterial transformation efficiency using plasmid artificial modification

We have developed a method to improve the transformation efficiency in genome-sequenced bacteria, using ‘Plasmid Artificial Modification’ (PAM), using the host's own restriction system. In this method, a shuttle vector was pre-methylated in Escherichia coli cells, which carry all the putative genes encoding the DNA modification enzymes of the target microorganism, before electroporation was performed. In the case of Bifidobacterium adolescentis ATCC15703 and pKKT427 (3.9 kb E. coli-Bifidobacterium shuttle vector), introducing two Type II DNA methyltransferase genes lead to an enhancement in the transformation efficiency by five orders of magnitude. This concept was also applicable to a Type I restriction system. In the case of Lactococcus lactis IO-1, by using PAM with a putative Type I methyltransferase system, hsdMS1, the transformation efficiency was improved by a factor of seven over that without PAM.
A comparison of dimensionality reduction techniques for the P300 response

Although P300 is a fairly stable response and therefore utilized in a wide variety of Brain Computer Interface (BCI) systems, the problems of feature selection and dimensionality reduction still constitute a major setback for the applications. In this study, we focus on the selection of best features of P300 data for decreasing the computation time, improving accuracy and visualizing both the underlying classification process and neurophysiological mechanism. To this end, the performance of three feature selection techniques are evaluated. The three techniques are Principle Component Analysis, Spatial Filters for Event Related Potentials and Recursive Channel Elimination. They are applied on the data set acquired through 4-class P300 experiments conducted on 5 subjects. The accuracy profile along with the computational issues are discussed.
Clustering-Based Task Scheduling in a Large Number of Heterogeneous Processors

Parallelization paradigms for effective execution in a Directed Acyclic Graph (DAG) application have been widely studied in the area of task scheduling. Schedule length can be varied depending on task assignment policies, scheduling policies, and heterogeneity in terms of each processor and each communication bandwidth in a heterogeneous system. One disadvantage of existing task scheduling algorithms is that the schedule length cannot be reduced for a data intensive application. In this paper, we propose a clustering-based task scheduling algorithm called Clustering for Minimizing the Worst Schedule Length (CMWSL) to minimize the schedule length in a large number of heterogeneous processors. First, the proposed method derives the lower bound of the total execution time for each processor by taking both the system and application characteristics into account. As a result, the number of processors used for actual execution is regulated to minimize the Worst Schedule Length (WSL). Then, the actual task assignment and task clustering are performed to minimize the schedule length until the total execution time in a task cluster exceeds the lower bound. Experimental results indicate that CMWSL outperforms both existing list-based and clustering-based task scheduling algorithms in terms of the schedule length and efficiency, especially in data-intensive applications.
MicroRNAs Role in the Central Nervous System Development andAbnormalities

Initially, non-coding RNAs were thought to be “junk RNAs”. MicroRNAs (miRNAs), a group of small non-coding RNA molecules, were discovered in 1993 [1,2]. However, their functions have been found to be associated with diverse biological procedures, including development, metabolism, immunity, hematopoietic differentiation, etc. [3]. It is estimated that miRNAs regulate the activity of approximately 60% of human protein-coding genes [4]. Abnormal miRNA expression may lead to a number of diseases, such as neoplasms [5,6], age-related diseases [7], and neurological disorders [8]. Advances in miRNA research have suggested that miRNAs not only help to understand molecular mechanisms of human CNS diseases, but also have potential to serve as biomarkers for early detection of neurological and/or neurodegenerative alterations.
A Five-Phase Reservation Protocol (FPRP) for Mobile Ad Hoc Networks

A new single channel, time division multiple access (TDMA)-based broadcast scheduling protocol, termed the Five-Phase Reservation Protocol (FPRP), is presented for mobile ad hoc networks. The protocol jointly and simultaneously performs the tasks of channel access and node broadcast scheduling. The protocol allows nodes to make reservations within TDMA broadcast schedules. It employs a contention-based mechanism with which nodes compete with each other to acquire TDMA slots. The FPRP is free of the “hidden terminal” problem, and is designed such that reservations can be made quickly and efficiently with negligible probability of conflict. It is fully-distributed and parallel (a reservation is made through a localized conversation between nodes in a 2-hop neighborhood), and is thus scalable. A “multihop ALOHA” policy is developed to support the FPRP. This policy uses a multihop, pseudo-Bayesian algorithm to calculate contention probabilities and enable faster convergence of the reservation procedure. The performance of the protocol, measured in terms of scheduling quality, scheduling overhead and robustness in the presence of nodal mobility, has been studied via simulations. The results showed that the protocol works very well in all three aspects. Some future work and applications are also discussed.
3D data codec and transmission over the internet

In this paper, a compression method of encoding/decoding 3D mesh based on octree is proposed. Vertices of the 3D mesh are reclassified according to the octree rule. We analyse all the nodes of the octree statistically to identify the type of nodes which accounts for the max proportion and encode them with fewer bits. According to the transmission sequence of geometric information, we rearrange topology and attribute information and encode them. Progressive strategies adopted by the single model and the scene are different in order to maximize the use of network bandwidth and computational performance of local machines. This method has high compression rate, is adapt to network transmission with short response time at the client and can control the level of detail of the model decoding.
Sensing the pulse of urban refueling behavior

Urban transportation is increasingly studied due to its complexity and economic importance. It is also a major component of urban energy use and pollution. The importance of this topic will only increase as urbanization continues around the world. A less researched aspect of transportation is the refueling behavior of drivers. In this paper, we propose a step toward real-time sensing of refueling behavior and citywide petrol consumption. We use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits, measure the time spent, and estimate overall demand. For times and stations with sparse data, we use collaborative filtering to estimate conditions. Our system provides real-time estimates of gas stations' waiting times, from which recommendations could be made, an indicator of overall gas usage, from which macro-scale economic decisions could be made, and a geographic view of the efficiency of gas station placement.