Miracle-dz/UniFAR-FTUs huggingface dataset:
A state based programming model for wireless sensor networks

Sensor nodes are small, inexpensive, and programmable devices that combine an autonomous power supply with computing, sensing, and wireless communication capabilities. Networks of sensor nodes can be deployed in the environment at a large scale to unobtrusively monitor phenomena of the real world. Wireless sensor networks are an emerging field of research with many potential applications. So far, however, only few applications have actually been realized. This is in part due to the lack of appropriate programming support, which makes the development of sensor-network applications tedious and error prone. This dissertation contributes a novel programming model and development environment for the efficient, modular, and well structured programming of wireless sensor nodes. Today there are two principal programming models used for sensor nodes, the multi-threaded model and the event-driven model. The multi-threaded model requires system support that is often considered too heavy for sensor nodes that operate at the low end of the resource spectrum. To cope with this issue, the event-driven model has been proposed. It requires very little runtime support by the system software and can thus be implemented even on the most constrained sensor nodes. The simple and lightweight approach to system software, however, tends to make event-driven applications in turn quite memory inefficient: Since the eventdriven model limits the use of local variables, programmers need to store temporary data in global variables. The memory of global variables, however, cannot easily and automatically be reused, hence the memory inefficiency. To counter this effect, programmers can resort to manual memory management, though this significantly affects program correctness and code modularity. In addition to its drawback of memory inefficiency, event-driven programming requires developers to manually keep track of the current program-state, which makes code modularization and debugging difficult, and leads to unstructured code. The key contribution of this dissertation is to show that the inadequacies of the event-driven model can be remedied without impairing its positive aspects, particularly its memory-efficient realization in sensor-node system software. Concretely, we present the Object State Model (OSM), a programming model that extends the event-driven programming paradigm with a notion of hierarchical and concurrent program states. Our thesis is that such a state-based model allows to specify well-structured, modular, and memory-efficient programs, yet requires as few runtime-resources as the event-driven model. To support this claim, we also present a programming environment based on the OSM model (including a programming language and compiler), as well as a sensor-node operating system capable of executing OSM programs.
Does the Causal Effect of Health on Employment Differ for Immigrants and Natives

This paper examines whether a causal effect of health on employment exists and, if so, whether it differs for immigrants and natives and whether such a difference can be attributed to different labour market status. Measuring poor health through information about hospital diagnoses for a number of specific diseases, we estimate bivariate probit models using the general practitioner’s referral behaviour as an instrument for receiving diagnoses. Using Danish administrative data, we find that poor health affects the employment probability negatively for both immigrants and native Danes. For men, the impact of health is largest for immigrants, while for women the effect is very similar. Differences in the distribution of lagged labour market status appear important only in explaining the results for women.
HST, VLT, and NTT imaging search for wide companions to bona-fide and candidate brown dwarfs in the Cha I dark cloud

We present results from a deep imaging search for companions around the young bona-fide and candidate brown dwarfs Cha H α 1 to 12 in the Cha I dark cloud, performed with HST WFPC2 ( R , I , H α ), VLT FORS1 ( VRI ), and NTT SofI ( JHK s ). We find 16 faint companion candidates around five primaries with separations between 1.5$^{\prime \prime}$ and 7$^{\prime \prime}$ and magnitudes in R & I from 19 to 25 mag, i.e. up to 8 mag fainter than the primaries. While most of these companion candidates are probably unrelated background objects, there is one promising candidate, namely $1.5^{\prime \prime}$ SW off the M6-dwarf Cha H α 5. This candidate is 3.8 to 4.7 mag fainter than the primary and its colors are consistent with an early- to mid-L spectral type. Assuming the same distance (140 pc) and absorption ( A I = 0.47 mag) as towards the primary, the companion candidate has $\log$ ($L_{\rm bol}/L_{\odot}) = -3.0 \pm 0.3$. At the age of the primary (1 to 5 Myrs), the faint object would have a mass of 3 to 15 Jupiter masses according to Burrows et al. ([CITE]) and Chabrier & Baraffe ([CITE]) models. The probability for this companion candidate to be an unrelated fore- or background object is ≤ $ 0.7\%$, its colors are marginally consistent with a strongly reddened background K giant. One other companion candidate has infrared colors consistent with an early T-dwarf. In addition, we present indications for Cha H α 2 being a close (~$ 0.2^{\prime \prime}$) binary with both components very close to the sub-stellar limit. Our detection limits are such that we should have detected all companions above ∼ 1 M jup with separations $\ge $ $2^{\prime \prime}$ ( ≥ $ 320$ AU) and all above ~$ 5$ M jup at ≥ $ 0.35^{\prime \prime}$ ( ≥ $ 50$ AU).
COVERAGE IN A HETEROGENEOUS SENSOR NETWORK

Wireless sensor network field has drawn attraction of researchers for its diversified applications. Due to the adhoc in nature of sensor network, conventional wireless network algorithms can not be applied directly. There are lot of problems which affect badly network performance and quality of service. Among these problems, lack of good coverage of network area is a major frustrating issue. Sensor network coverage issue becomes more complicated when there are different types of sensors exist in a same field. If the network nodes vary in their properties coverage problem becomes more complicated. In [9], the authors gave an upper and lower bound of coverage using Voronoi diagram. But they only considered a homogeneous network. Their solution becomes challenging for heterogeneous network. In our paper we give a solution to handle heterogeneous sensor network where sensors are not isotropic.
Legible robot pointing

Good communication is critical to seamless human-robot interaction. Among numerous communication channels, here we focus on gestures, and in particular on spacial deixis: pointing at objects in the environment in order to reference them. We propose a mathematical model that enables robots to generate pointing configurations that make the goal object as clear as possible - pointing configurations that are legible. We study the implications of legibility on pointing, e.g. that the robot will sometimes need to trade off efficiency for the sake of clarity. Finally, we test how well our model works in practice in a series of user studies, showing that the resulting pointing configurations make the goal object easier to infer for novice users.
Modelling View-count Dynamics in YouTube

The goal of this paper is to study the behaviour of view-count in YouTube. We first propose several bio-inspired models for the evolution of the view-count of YouTube videos. We show, using a large set of empirical data, that the view-count for 90% of videos in YouTube can indeed be associated to at least one of these models, with a Mean Error which does not exceed 5%. We derive automatic ways of classifying the view-count curve into one of these models and of extracting the most suitable parameters of the model. We study empirically the impact of videos' popularity and category on the evolution of its view-count. We finally use the above classification along with the automatic parameters extraction in order to predict the evolution of videos' view-count.
Information, noise, coding, modulation: What about the brain?

At the microscopic level, the brain is fundamentally a matter of physics and chemistry, as all the components of the universe are. At the macroscopic scale, behavior, psychology and affects are the main dimensions of its life. To convert atoms and molecules into intelligence, some kind of information has to be fixed in the grey matter of the cerebral cortex. The way this “mental information” is materialized and processed is still an enigma, probably the most puzzling problem addressed to science nowadays. At this mesoscopic level of the brain functioning, the concepts to consider are likely the same as those considered in communication and information theory, mainly information, noise, coding and modulation. This paper proposes some ideas that could help understand some features of the brain in an information-processing perspective.
A metabolic phenotyping approach to understanding relationships between metabolic syndrome and breast tumour responses to chemotherapy.

PURPOSE Breast cancer is associated with adverse outcomes in patients with the metabolic syndrome phenotype. To study this further, we examined the relationship between serum metabolite levels and the components of metabolic syndrome with treatment outcomes in breast cancer. METHODS A total of 88 women with measurable breast cancer were studied; their serum metabolites as assessed by (1)H nuclear magnetic resonance spectroscopy, blood pressure, lipids, glucose, body mass index and waist circumference were recorded and correlated with treatment response. RESULTS We identified metabolic syndrome in approximately half of our cohort (42 patients) and observed a significant trend (P = 0.03) of increased incidence of metabolic syndrome in partial response (33.3%), stable disease (42.9%) and progressive disease groups (66.1%). High blood sugar predicted a poor response (P < 0.001). Logistic regression of metabonomic data demonstrated that high lactate (P = 0.03) and low alanine (P = 0.01) combined with high glucose (P = 0.01) were associated with disease progression. CONCLUSIONS Metabolic syndrome is commonly observed in metastatic breast cancer and these patients have poorer outcomes. These data, which support our previous findings, suggest that high blood glucose as part of metabolic syndrome is associated with a poor response in breast cancer. They also validate new therapeutic approaches that focus on metabolism.
Application of Network Analyzer Based on the Automatic Control of LabVIEW in Radio and Television Test

A control system of network analyzer based on LabVIEW is introduced in the paper. The system connects PC and network analyzer through USB/GPIB bus, and then realizes the control operation of control instruction using SCPI control instructions, finally, completes the measurement of reflection loss, insertion loss and band flatness. The automatic test system has the advantages of high reliability and practicality.
Xerox TREC-5 Site Report: Routing, Filtering, NLP, and Spanish Tracks

Xerox participated in TREC-5 through experiments carried out separately and conjointly at the Ranx Xerox Research Centre in Grenoble and the Xerox Palo Alto Research Center. The remainder of this report is divided into three sections. The first section describes the work on routing and filtering, the second section covers the NLP track and the final section addresses the Spanish track
The effects of elevated CO2 concentration on competitive interaction between aceticlastic and syntrophic methanogenesis in a model microbial consortium

Investigation of microbial interspecies interactions is essential for elucidating the function and stability of microbial ecosystems. However, community-based analyses including molecular-fingerprinting methods have limitations for precise understanding of interspecies interactions. Construction of model microbial consortia consisting of defined mixed cultures of isolated microorganisms is an excellent method for research on interspecies interactions. In this study, a model microbial consortium consisting of microorganisms that convert acetate into methane directly (Methanosaeta thermophila) and syntrophically (Thermacetogenium phaeum and Methanothermobacter thermautotrophicus) was constructed and the effects of elevated CO2 concentrations on intermicrobial competition were investigated. Analyses on the community dynamics by quantitative RT-PCR and fluorescent in situ hybridization targeting their 16S rRNAs revealed that high concentrations of CO2 have suppressive effects on the syntrophic microorganisms, but not on the aceticlastic methanogen. The pathways were further characterized by determining the Gibbs free energy changes (ΔG) of the metabolic reactions conducted by each microorganism under different CO2 concentrations. The ΔG value of the acetate oxidation reaction (T. phaeum) under high CO2 conditions became significantly higher than -20 kJ per mol of acetate, which is the borderline level for sustaining microbial growth. These results suggest that high concentrations of CO2 undermine energy acquisition of T. phaeum, resulting in dominance of the aceticlastic methanogen. This study demonstrates that investigation on model microbial consortia is useful for untangling microbial interspecies interactions, including competition among microorganisms occupying the same trophic niche in complex microbial ecosystems.
Power control and management in the NetFPGA gigabit router

Improving energy efficiency in Internet equipments is becoming an increasingly important research topic. In this paper, we discuss the lesson gained by experimenting a power management mechanism aimed at reducing the power consumption while guaranteeing the Quality of Service (QoS). To this purpose, we drive the clock frequency of an open flexible and high-performance router, the NetFPGA, according to the measured input bit rate. An evaluation of the proposed power management policy is provided.
Improving self-regulation and executive functioning skills in primary school children in a remote Australian Aboriginal community: A pilot study of the Alert Program®

Self-regulation and executive functioning impairments are common in children with fetal alcohol spectrum disorder. Given the high rates of fetal alcohol spectrum disorder identified amongst children living in the remote Fitzroy Valley region of Western Australia, the Alert Program® was identified as a culturally safe intervention for use in local primary schools. Researchers collaborated with Aboriginal Elders, community members, and staff from a Fitzroy Valley primary school to trial the Alert Program®. Teachers were trained to deliver eight Alert Program® lessons to children in class. Self-regulation and executive functioning were measured using teacher and parent/caregiver questionnaires three times. Data were analyzed using repeated-measures ANOVA. Teacher-led delivery of the Alert Program® was feasible in a region with high fetal alcohol spectrum disorder rates. As measured by teacher and parent/caregiver ratings, this curriculum may improve the self-regulation and executive functioning of children for some outcomes and provide sustained effects for some children. This community partnered pilot research, evaluated a school-based program to reduce the behavioral impact of fetal alcohol spectrum disorder, and informed design of a larger trial across eight Aboriginal community schools.
Hepatitis C Virus Genotype 4 Resistance and Subtype Demographic Characterization of Patients Treated with Ombitasvir plus Paritaprevir/Ritonavir

ABSTRACT Hepatitis C virus (HCV) genotype 4 (GT4) is genetically diverse, with 17 confirmed subtypes, and comprises approximately 13% of infections worldwide. In this study, we identified GT4 subtypes by phylogenetic analysis, assessed differences in patient demographics across GT4 subtypes, examined baseline sequence variability among subtypes and the potential impact on treatment outcome, and analyzed the development of viral resistance in patients who received a regimen of ombitasvir (nonstructural protein 5A [NS5A] inhibitor) plus ritonavir-boosted paritaprevir (NS3/4A inhibitor) with or without ribavirin (RBV) for the treatment of HCV GT4 infection. Phylogenetic analysis of HCV NS3/4A, NS5A, and NS5B nucleotide sequences identified 7 subtypes (4a, 4b, 4c, 4d, 4f, 4g/4k, and 4o) among 132 patient samples. Subtype prevalence varied by country, and the distributions of patient birth cohort and race were significantly different across GT4 subtypes 4a, 4d, and non-4a/4d. Baseline amino acid variability was detected in NS5A across GT4 subtypes but had no impact on treatment outcome. Three patients experienced virologic failure and were infected with subtype 4d, and the predominant resistance-associated variants at the time of failure were D168V in NS3 and L28V in NS5A. Overall, high response rates were observed among patients infected with 7 HCV GT4 subtypes, with no impact of baseline variants on treatment outcome. GT4 subtype distribution in this study differed based on patient demographics and geography.
Investigating the Role of User Engagement in Digital Reading Environments

User engagement is recognized as an important component of the user experience, but relatively little is known about the effect of engagement on the learning outcomes of such interactions. This experimental user study examines the relationship between user engagement (UE) and comprehension in varied academic reading environments. Forty-one university students interacted with one of two sets of texts presented in 4 conditions in the context of preparing for a class assignment. Employing the User Engagement Scale (UES), we found evidence of a relationship between students' comprehension of the texts and their degree of engagement with them. However, this association was confined to one of the UES subscales and was not consistent across levels of engagement. An examination of additional variables found little evidence that system and content characteristics influenced engagement; however, we noted that all students' reported increased knowledge, but topical interest for non-engaged students declined. Results contribute to existing literature by adding further evidence that the relationship between engagement and comprehension is complex and mediated.
Visibility graph analysis on EEG signal

We use the visibility graph analysis to research EEG signal and construct the complex network of EEG signal. In the paper, we construct the network by using EEG signal of the people with occupational stress and the normal people. Some properties of the complex network are found in this paper. The results of our experiment show that the properties of the normal people and the people with occupational stress are different.
The Computational Structure of Monotone Monadic SNP and Constraint Satisfaction: A Study through Datalog and Group Theory

This paper starts with the project of finding a large subclass of NP which exhibits a dichotomy. The approach is to find this subclass via syntactic prescriptions. While the paper does not achieve this goal, it does isolate a class (of problems specified by) "monotone monadic SNP without inequality" which may exhibit this dichotomy. We justify the placing of all these restrictions by showing, essentially using Ladner's theorem, that classes obtained by using only two of the above three restrictions do not show this dichotomy. We then explore the structure of this class. We show that all problems in this class reduce to the seemingly simpler class CSP. We divide CSP into subclasses and try to unify the collection of all known polytime algorithms for CSP problems and extract properties that make CSP problems NP-hard. This is where the second part of the title, "a study through Datalog and group theory," comes in. We present conjectures about this class which would end in showing the dichotomy.
Efficient resource management for hard real-time communication over differentiated services architectures

We propose an efficient strategy for resource management for scalable QoS guaranteed real-time communication services. This strategy is based on sink trees, and is particularly well suited for differentiated-services based architectures. We first show that finding a set of sink-trees in a given network is NP-complete. Then we propose a heuristic algorithm that always efficiently produces a set of sink-trees for a given network. Sink-tree based resource management integrates routing and resource reservation along the routes, and therefore has a number of advantages over other resource management scheme, in terms of: admission probability, link resource utilization, flow set up latency, signaling overhead, and routing over-head. In this paper we show by simulation experiments that even for simple cases the sink-tree based approach shows excellent results in terms of admission probability.
BAYESIAN THEORY WITH APPLICATION TO CUSTOMER SEGMENT AND CONSUMING BEHAVIOR CONTROL FOR ONLINE BUSINESS

ABSTRACT In this paper, we follow the model of interpurchase times to achieve heterogeneity across customers. We employ a mixture model to segment customers into three states: super-active, active and inactive. The interpurchase model and mixture model are solved by the hierarchical Bayes via Markov Chain Monte Carlo method. We employ CUSUM chart based on the density of active state to monitor consumer behavior. Factors on the CUSUM chart include interpurchase time and recency (the interval from the last visit day to the analyzing day) for individual customers. Control charts such as CUSUM chart have been used by industries to control product quality. They are useful time-series tools enable supervisors to correct the process in time when the chart shows a significant warning signal. In this research, we combined two information, interpurchase time and recency, from an individual customer and use CUSUM chart to control the consuming behavior. A real case study from an online company employed on CUSUM chart shows the type I and type II errors are less than 5% and 10%, respectively.
A comparative analysis of SVM and its stacking with other classification algorithm for intrusion detection

Network attacks have become more pervasive in the cyber world. There are various attacks such as denial of service, scanning, privilege escalation that is increasing day by day leading towards the requirement of a more robust and adaptable security techniques. Anomaly detection is the main focus of our paper. Support Vector Machine (SVM) is one of the good classification algorithm applied specially for intrusion detection. However, its performance can be significantly improved when it is applied in integration with other classifiers. In this paper, we have performed a comparative analysis of SVM classifier's performance when it is stacked with other classifiers like BayesNet, AdaBoost, Logistic, IBK, J48, RandomForest, JRip, OneR and SimpleCart. Multi-Classifier algorithm have better classification power when compared to a single classifier algorithm specially for detecting low frequency attacks such as guess password, rootkits, spyware etc. Our preliminary analysis over NSL-KDD'99 dataset shows that stacking of SVM and Random Forest provides the best performance with accuracy of around 97.50% which apparently better than SVM (91.81%).