Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic topology optimization for dependable sensor networks

In this paper, wireless sensor network (WSN) topology optimization for dependability is considered, where two metrics “reliability” and “integrity” for WSN are defined. First, static topology optimization problem for reliability, integrity and joint optimization of them are investigated and some trade-off relationships are clarified. Then, dynamic topology optimization problem is proposed, where some node connections to their parents' nodes change dynamically with a stochastic distribution rate. Then a heuristic optimization method and some functions for each node to realize a decentralized optimization are proposed. Finally some numerical examples are illustrated to demonstrate the effectiveness of these optimization methods and the optimal WSN topology in various optimization problems.
Short-critical-path and structurally orthogonal scaled CORDIC-based approximations of the eight-point discrete cosine transform

A family of multiplierless transforms is presented that approximate the eight-point type-II discrete cosine transform (DCT) as accurately as the state-of-the-art scaled DCT schemes, but having 14-17% shorter critical paths (1/6 or 1/7 less adders). Compared to the existing solutions that use the coordinate rotation digital computer (CORDIC) algorithm, the advantage of higher throughput is accompanied by saving additions. Only some lifting-based BinDCT schemes require less adders in total, in spite of longer critical paths. The transforms have been derived from the fast Loeffler's algorithm by replacing the rotation stage with unfolded CORDIC iterations, which have been arranged so that two rotation approximations use the same scaling. This is equivalent to imposing structural orthogonality (losslessness) on a system, from which the scaling can then be extracted so as to shorten the critical path. Supporting ideas are a notation for more conveniently describing CORDIC circuits, and an angle conversion that allows rotations to be approximated using an extended set of CORDIC circuits. The research results have been validated by field programmable gate array-based hardware design experiments and by usability tests based on a software JPEG codec.
An interposed 2-Level I/O scheduling framework for performance virtualization

Networked storage and large disk arrays are enabling consolidated storage systems. While such consolidation is attractive economically, sharing of the underlying storage infrastructure can lead to interference between the different applications/users and possible violation of performance-based service level objectives (SLO). The data centers aim to insulate the users from each other (i.e., referred to as performance virtualization), giving each user the impression that the storage utility is dedicated to them. Several approaches exist for performance virtualization. We categorize them as: (1) schemes that use a proportional bandwidth sharing paradigm, such as SFQ(D), FSFQ(D) [3], and CVC [2]; and (2) schemes that use feedback-based control, such as Triage [4], SLEDS [1], and Facade [5]. These schemes have various advantages, but suffer from one or more of the following drawbacks: (i) rely on a fairly detailed performance model of the underlying storage system to estimate the service time of an individual request [2, 3], (ii) couple rate and latency allocation in a single scheduler making them less flexible [2, 3], (iii) may not always exploit the full bandwidth offered by the storage system [1, 4], or (iv) may not provide good performance isolation in the overloaded situations [5]. Our scheme aim to provide both throughput and latency guarantees, because each of them is critical to many (if not all) applications. We propose an interposed 2-level scheduling framework that separates rate allocation from latency control rather than couple them together. In our system, incoming I/O requests are classified into different classes, with an SLO pre-determined for each class. Without loss of generality, we assume that each user belongs to a different class. The SLO is a tuple for each class. R represents the maximum arrival rate with a latency guarantee D for a given class. If the arrival rate for that class is higher than R, then its throughput should be at least R, but there is no latency guarantee. In our approach, we use a statistical latency guarantee, that is the SLO requiring x% (95% in our evaluation) of all requests to be bounded by a latency of D. This SLO is enforced in each time
Spammer Detection for Real-Time Big Data Graphs

Prodigious explosion of social network services may trigger new business models, there trails, however, negative aspects such as personal information spill or spamming as much. Amongst conventional spam detection approaches, the studies based on vertex Degrees have been sacrificed false negative results so that normal vertices can be specified as spammer ones. In this paper, we propose a novel approach by applying the circuit structure in the social networks,, which demonstrates the advantages of our work in the experiment.
A model for the optical high state light curve of AM Herculis

We present a simple quantitative model that can describe the photometric B and V band light curves of AM Herculis obtained during a high state. The double-humped shape of the V band light curve is dominated by cyclotron emission from a region at the main accreting pole with an area of ~$5\times10^{16} \mathrm{cm^2}$ and sustaining an inflow of ~0.06 $\rm g cm^{-2} s^{-1}$. The almost unmodulated B band is dominated by emission from the accretion stream. The contribution of the heated white dwarf to the optical emission is small in the B band, but comparable to that of the accretion stream in the V band. The emission of the secondary star is negligible both in B and in V .
Cardiovascular Reactivity to Video Game Predicts Subsequent Blood Pressure Increases in Young Men: The CARDIA Study

Objective This study was undertaken to determine the relationship between heightened reactivity of blood pressure (BP) during stress and 5-year changes in blood pressure and hypertensive status, using the CARDIA study. Method A total of 3364 participants (910 white men, 909 white women, 678 black men, and 867 black women), initially 20 to 32 years old and normotensive, were included. Cardiovascular reactivity to psychological stressors (video game and star-tracing tasks for 3 minutes, cold pressor test for 1 minute) was measured in 1987-1988. We then examined reactivity as a predictor of significant BP change (>or=to8 mm Hg, thought to represent a clinically significant increase) over the next 5 years. Logistic regression models were used to control for potential covariates. Significant BP change and the development of hypertension (BP greater than 140/90 or taking medication for hypertension) over the 5-year follow-up were examined in separate analyses. Results Increased systolic blood pressure (SBP) reactivity to the video game was associated with a significant 5-year SBP increase among the entire cohort, independent of resting SBP (p < .0001). Subsequent analyses showed that this relationship held for men but not for women. Reactivity to the star-tracing task or the cold pressor test did not predict significant BP change. Among black men only, new hypertensives (N = 36) had greater diastolic blood pressure (DBP) reactivity to the video game (p = .01). Conclusions: Although BP reactivity to all physical and mental stressors used in this study did not consistently predict 5-year change in BP in this young cohort, the results indicate that reactivity to a video game stressor predicts 5-year change in BP and early hypertension among young adult men. These findings are consistent with other studies showing the usefulness of stressors producing a primarily beta-adrenergic response in predicting BP change and hypertension. The results may be limited by the shortened initial rest and recovery periods used in the CARDIA protocol.
Recent advances in 3D flame tomography

To reduce greenhouse gas emissions from fossil fuel fired power plants a range of new combustion technologies are being developed or refined, including oxy­fuel combustion, co­firing biomass with coal and fluidized bed combustion. Flame characteristics under such combustion conditions are expected to be different from those in normal air fired combustion processes. Quantified flame characteristics such as temperature distribution, oscillation frequency, and ignition volume play an important part in the optimized design and operation of the environmentally friendly power generation systems. However, it is challenging to obtain such flame characteristics particularly through a three­ dimensional (3D), non­intrusive means. Various 3D tomography methods have been proposed to visualize and characterize combustion flames. These include passive optical tomography, laser based tomography, and electrical tomography. This paper identifies the challenges in 3D flame tomography and reviews the existing techniques for quantitative characterization of combustion flames. Future trends in 3D flame tomography for applications in the power generation industry are discussed.
Image segmentation from texture measurement

It is known that the human visual system, unsurpassed in its ability to reconstruct surfaces, employs different cues to solve this difficult task. The prevailing method in digital photogrammetry is stereopsis. However, texture may provide valuable information about the shape of surfaces. In this paper we employ Laws' method of texture energy transforms to extract texture information from digital aerial imagery. The images are convolved with micro-texture filt~rs to obtain local texture properties. Each micro-texture feature plane is transformed into an texture energy image by nlOving-window to render macro-texture features. Finally, the macro-texture feature planes are combined and then clustered into regions of similar texture pattern. The method is implemented in a scale-space approach, and the boundaries obtained from texture are compared with physical boundaries of the image.
Crowdsourcing Framework for Acoustic Sound Based Obstacle Echolocation through Smartphones for Visually Impaired

Guiding and assisting the visually impaired has been a significant problem. Yet, this problem has received insufficient attention. With the pervasion of smartphones, we attempt to utilize their powerful abilities to assist the visually impaired. Existing applications for assisting the visually impaired are mostly obstacle detection, most of which rely on image processing or pattern recognition techniques. However, these techniques are either too slow to achieve real-time processing or impractical due to heavy computational and storage overheads. In this paper, we first propose an acoustic sound based obstacle echolocation ranging method using chirp signals. Next, we present a crowdsourcing framework for obstacle detection, where the surrounding smartphones are utilized as an array of sensors for better results. Combining this framework with a modified TDOA algorithm and Least-Squares method, we thus propose a LsTDOA scheme to effectively locate 3-D coordinates of the obstacle. Furthermore, we take the priori knowledge of possible errors into consideration, and then propose weighted-LsTDOA(WLsTDOA) to improve the accuracy of obstacle estimation. We perform simulation experiments to demonstrate the accuracy and reliability of our scheme, which can be achieved in real-time.
Comparison of Energy Detection Methods in Cognitive Radio Networks

Cognitive Radio is an emerging new paradigm in wireless communications. Its goal is to make frequency use more efficient by using temporarily unoccupied frequency bands. Therefore frequency bands have to be measured in order to decide about the occupation of the band. One of the used techniques is energy detection. In this paper different energy detection models were compared. The evaluation was performed using a simulator for an OFDM-modulation based wireless communication network.
Sexual health of Latino migrant day labourers under conditions of structural vulnerability

The purpose of this paper is to explore the context of the sexual health of Latino migrant day labourers in the USA, challenges to sexual health and ways of coping, with attention to conditions of structural vulnerability permeating the lives of this unique Latino population. Given the limited information about this topic and population, ethnographic research employing in-depth semi-structured interviews with 51 labourers, recruited through purposive sampling in the San Francisco Bay Area, was utilised. The sexual health aspirations of the men are deeply embedded in the core value and practice of Latino familismo or, in this case, the central goal of securing a family headed by men as providers and present husbands/fathers. However, such goals are frequently thwarted by the poverty engendering work and prolonged separations from home that characterise predominantly undocumented day labour in the USA. Resulting goal frustration, combined with pent up sexual urges, often lead to sexual risk in spite of efforts to cope with challenges to sexual health. Unless community-, state- and national-level interventions are developed to mitigate the pronounced structural vulnerability of migrant day labourers, individual level interventions to promote sexual health, and decrease risk and distress, are likely to have diminishing returns.
Detecting time series motifs under uniform scaling

Time series motifs are approximately repeated patterns foundwithin the data. Such motifs have utility for many data mining algorithms, including rule-discovery,novelty-detection, summarization and clustering. Since the formalization of the problem and the introduction of efficient linear time algorithms, motif discovery has been successfully applied tomany domains, including medicine, motion capture, robotics and meteorology. In this work we show that most previous applications of time series motifs have been severely limited by the definition's brittleness to even slight changes of uniform scaling, the speed at which the patterns develop. We introduce a new algorithm that allows discovery of time series motifs with invariance to uniform scaling, and show that it produces objectively superior results in several important domains. Apart from being more general than all other motifdiscovery algorithms, a further contribution of our work isthat it is simpler than previous approaches, in particular we have drastically reduced the number of parameters that need to be specified.
Smart phone based systems for social psychological research: challenges and design guidelines

Social psychology research deals with understanding many aspects of human behavior, and this helps not only to gain insights into this complex phenomenon but also to provide useful feedback to the participants. Social psychological research is mainly conducted through self-reports and surveys, however, this methodology is laborious and requires considerable offline analysis. Moreover, self-reports are also found to be biased towards pleasant experiences. Mobile phones represent a perfect platform for conducting social psychological research as they are ubiquitous, unobtrusive, and sensor-rich devices. However, limited battery and computing power, and expensive data plans make it difficult to support various demanding sensing and computation requirements of the social psychological research. In this paper, we describe the specific challenges in building systems based on off-the-shelf mobile phones for conducting social experiments, and provide design guidelines based on our recent works for implementing such systems.
The Constrained CramÉr–Rao Bound From the Perspective of Fitting a Model

Stoica and Ng (1998) presented a simple expression for the constrained Cramer-Rao bound (CCRB) when the constraints are given by a differentiable function of the parameter to be estimated. This letter considers the parallel case in developing the CCRB when the parameters are locally fitted to a lower-dimensional parametric model, i.e., the parameters are locally assumed to be functions of a distinct reduced parameter vector. We employ classical elements of CRB theory on the locally fitted model to present a very simple derivation of the CCRB, conditions for attaining the bound, and a regularity condition. Examples illustrate the key ideas.
Efficient data collection with directional antenna and network coding in wireless sensor networks

In wireless sensor networks (WSNs), with converge-cast, the data flow pattern forms a funnel structure and converges at the Sink. Reduction of traffic flow to the Sink cuts down the funneling effect and improves the efficiency of the network. This paper presents an energy Efficient Triangular (regular) Deployment strategy with Directional Antenna (ETD-DA) while analyzing the directivity of antenna beam in the network. A triangular deployment strategy, where nodes are placed at different layers, has been adopted in view of the funneling effect of traffic in a single Sink network. A particular layer of ETD-DA involves in sensing and generating data for an event in WSN. On the proposed pattern, ETD-DA, the inter-level communication has been analyzed. The 2-connectivity pattern of communication has been achieved by orienting the directional antenna beam of a sensor in a particular direction towards the Sink. For the improvement of reliability (protection of data against path failure) of the network, a network coding based forwarding strategy has also been proposed for inter level communication in the ETD-DA with 2-connectivity pattern. The energy consumption has been significantly reduced due to the combination of directional beam and network coding. A detailed theoretical analysis and simulation have been performed to show the efficacy of the proposed approach.
Contribution of Xanthine Oxidoreductase to Mammary Epithelial and Breast Cancer Cell Differentiation In Part Modulates Inhibitor of Differentiation-1

Loss of xanthine oxidoreductase (XOR) has been linked to aggressive breast cancer in vivo and to breast cancer cell aggressiveness in vitro. In the present study, we hypothesized that the contribution of XOR to the development of the normal mammary gland may underlie its capacity to modulate breast cancer. We contrasted in vitro and in vivo developmental systems by differentiation marker and microarray analyses. Human breast cancer microarray was used for clinical outcome studies. The role of XOR in differentiation and proliferation was examined in human breast cancer cells and in a mouse xenograft model. Our data show that XOR was required for functional differentiation of mammary epithelial cells both in vitro and in vivo. Poor XOR expression was observed in a mouse ErbB2 breast cancer model, and pharmacologic inhibition of XOR increased breast cancer tumor burden in mouse xenograft. mRNA microarray analysis of human breast cancer revealed that low XOR expression was significantly associated with time to tumor relapse. The opposing expression of XOR and inhibitor of differentiation-1 (Id1) during HC11 differentiation and mammary gland development suggested a potential functional relationship. While overexpression of Id1 inhibited HC11 differentiation and XOR expression, XOR itself modulated expression of Id1 in differentiating HC11 cells. Overexpression of XOR both inhibited Id1-induced proliferation and -stimulated differentiation of Heregulin-β1–treated human breast cancer cells. These results show that XOR is an important functional component of differentiation whose diminished expression contributes to breast cancer aggressiveness, and they support XOR as both a breast cancer biomarker and a target for pharmacologic activation in therapeutic management of aggressive breast cancer. Mol Cancer Res; 9(9); 1242–54. ©2011 AACR.
Maximising the value of Portable XRF data in exploration: An example from Marirongoe, Mozambique

Here we present a portable X-ray fluorescence (pXRF) dataset collected in situ (n = 1591) and a laboratory dataset (n = 226) from a soil sampling campaign in Marirongoe, Mozambique, to document the strength of rapid geochemical data collection in the field during mineral exploration. Real-time mapping of the geochemistry of underlying granite by utilising pXRF analysis of soil samples identified variation in granitic composition, thus allowing exploration to rapidly focus on the most prospective areas for Ta-Nb-U-REE mineralization. Principal components analysis and clustering protocols are applied to the centred log-ratio transform of a selection of eight elements (Ca, Fe, K, Rb, Sr, Ti, Mn, and Zr) to identify rocks of the same geochemical affinity a posteriori. Maps of these clusters reveal a map pattern that provides an interpretation of the underlying geology. One of the limitations of pXRF is false elemental concentrations being detected due to spectral overlaps between elements. We provide a possible solution to this problem through statistical data analysis using a probabilistic modelling approach. We propose a binary approach whereby the pXRF data for these elements, such as Sn, can be considered in the context of presence (detected; >150 ppm) or absence (not detected; <150 ppm) as comparison to laboratory data shows that the concentration of Sn is reliably detected at concentrations >150 ppm. A kernel density estimator and Bayes conditional probability can provide an effective method for calculating the probability of a sample having elevated content of elements, such as Sn, which may be variably detected by pXRF (depending on matrix and concentration). Utilising statistical approaches to treat large geochemical datasets, such as those that can be generated by pXRF, as they are collected, can provide timely and significant insights that might otherwise not have been apparent in elemental concentration maps alone.
A Privacy Protection Method for Social Network Data against Content/Degree Attacks

Recently, social network services are rapidly growing and this trend is expected to continue in the future. Social network data can be published for various purposes such as statistical analysis and population studies. When social network data are published, however, the privacy of some people may be disclosed. The most straightforward manner to preserve privacy in social network data is to remove the identifiers of persons from the social network data. However, an adversary can infer the identity of a person in the social network by using his/her background knowledge, which consists of content information such as the age, sex, or address of the person and structural information such as the number of persons having a relationship with the person. In this paper, we propose a privacy protection method for social network data. The proposed method anonymizes social network data to prevent privacy attacks that use both content and structural information, while minimizing the information loss or distortion of the anonymized social network data. Through extensive experiments, we verify the effectiveness and applicability of the proposed method.