Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing a Ripple Effect Measure: A Theoretical and Empirical Validation

Context: Change impact analysis investigates the negative consequence of system changes, i.e., the propagation of changes to other parts of the system (also known as the ripple effect). Identifying modules of the system that will be affected by the ripple effect is an important activity, before and after the application of any change. Goal: However, in the literature, there is only a limited set of studies that investigate the probability of a random change occurring in one class, to propagate to another. In this paper we discuss and evaluate the Ripple Effect Measure (in short REM), a metric that can be used to assess the aforementioned probability. Method: To evaluate the capacity of REM as an assessor of the prob-ability of a class to change due to the ripple effect, we: (a) mathematically validate it against established metric properties (e.g., non-negativity, monotonicity, etc.), proposed by Briand et al., and (b) empirically investigate its validity as an assessor of class proneness to the ripple effect, based on the 1061-1998 IEEE Standard on Software Measurement (e.g., correlation, predictive power, etc.). To apply the empirical validation process, we conducted a holistic multiple-case study on java open-source classes. Results: The results of REM validation (both mathematical and empirical) suggest that REM is a theoretically sound measure that is the most valid assessor of the probability of a class to change due to the ripple effect, compared to other existing metrics.
Optimizing Remote Sensing and GIS Tools for Mapping and Managing the Distribution of an Invasive Mangrove (Rhizophora mangle) on South Molokai, Hawaii

In 1902, the Florida red mangrove, Rhizophora mangle L. , was introduced to the island of Molokai, Hawaii, and has since colonized nearly 25% of the south coast shoreline. By classifying three kinds of remote sensing imagery, we compared abilities to detect invasive mangrove distributions and to discriminate mangroves from surrounding terrestrial vegetation. Using three analytical techniques, we compared mangrove mapping accuracy for various sensor-technique combinations. ANOVA of accuracy assessments demonstrated significant differences among techniques, but no significant differences among the three sensors. We summarize advantages and disadvantages of each sensor and technique for mapping mangrove distributions in tropical coastal environments.
A Probabilistic Model for Robust Localization Based on a Binaural Auditory Front-End

Although extensive research has been done in the field of machine-based localization, the degrading effect of reverberation and the presence of multiple sources on localization performance has remained a major problem. Motivated by the ability of the human auditory system to robustly analyze complex acoustic scenes, the associated peripheral stage is used in this paper as a front-end to estimate the azimuth of sound sources based on binaural signals. One classical approach to localize an acoustic source in the horizontal plane is to estimate the interaural time difference (ITD) between both ears by searching for the maximum in the cross-correlation function. Apart from ITDs, the interaural level difference (ILD) can contribute to localization, especially at higher frequencies where the wavelength becomes smaller than the diameter of the head, leading to ambiguous ITD information. The interdependency of ITD and ILD on azimuth is a complex pattern that depends also on the room acoustics, and is therefore learned by azimuth-dependent Gaussian mixture models (GMMs). Multiconditional training is performed to take into account the variability of the binaural features which results from multiple sources and the effect of reverberation. The proposed localization model outperforms state-of-the-art localization techniques in simulated adverse acoustic conditions.
Covert single-hop communication in a wireless network with distributed artificial noise generation

Covert communication, also known as low probability of detection (LPD) communication, prevents the adversary from knowing that a communication is taking place. Recent work has demonstrated that, in a three-party scenario with a transmitter (Alice), intended recipient (Bob), and adversary (Warden Willie), the maximum number of bits that can be transmitted reliably from Alice to Bob without detection by Willie, when additive white Gaussian noise (AWGN) channels exist between all parties, is on the order of the square root of the number of channel uses. In this paper, we begin consideration of network scenarios by studying the case where there are additional “friendly” nodes present in the environment that can produce artificial noise to aid in hiding the communication. We establish achievability results by considering constructions where the system node closest to the warden produces artificial noise and demonstrate a significant improvement in the throughput achieved covertly, without requiring close coordination between Alice and the noise-generating node. Conversely, under mild restrictions on the communication strategy, we demonstrate no higher covert throughput is possible. Extensions to the consideration of the achievable covert throughput when multiple wardens randomly located in the environment collaborate to attempt detection of the transmitter are also considered.
Neopterin as an Effect Modifier of the Cardiovascular Risk Predicted by Total Homocysteine: A Prospective 2‐Cohort Study

Background Plasma total homocysteine (tHcy) is related to plasma neopterin, an indicator of interferon‐γ‐mediated immune activation, and both biomarkers positively predict cardiovascular risk. We examined whether the association between tHcy and subsequent risk of acute myocardial infarction (AMI) was modified by systemic concentrations of neopterin and C‐reactive protein among patients with coronary heart disease. Methods and Results By Cox modeling, we explored the association between tHcy and risk of AMI in 4164 patients with suspected stable angina pectoris. Subgroup analyses were performed according to median levels of neopterin and C‐reactive protein. A replication study was performed among 3749 patients with AMI at baseline. Median follow‐up was 7.3 and 8.3 years among patients with stable angina pectoris and AMI, respectively. tHcy and neopterin correlated in both cohorts (r s=0.34 and r s=0.30 among stable angina pectoris and AMI patients, respectively, both P<0.001). tHcy predicted AMI in both cohorts, independent of B‐vitamin treatment. However, significant risk associations were confined to patients with plasma neopterin above the median (hazard ratios [95% confidence interval] per 1‐SD increment of log‐transformed tHcy 1.38 [1.26–1.50] and 1.18 [1.10–1.26] among stable angina pectoris and AMI patients, respectively) (P int<0.005 in both cohorts). Further, adding information on the interaction between tHcy and neopterin improved model discrimination and reclassification. tHcy and C‐reactive protein were weakly related, and no effect modification was found by C‐reactive protein. Conclusions Among patients with coronary heart disease, tHcy predicted risk of AMI only in subjects with concomitantly elevated plasma neopterin. Our results motivate further research on the relationship between homocysteine metabolism, cellular immune activation, and atherothrombosis.
A global monthly climatology of phosphate, nitrate, and silicate in the upper ocean: Spring‐summer export production and shallow remineralization

We have created monthly climatologies of nutrients in the upper 500 m of the ocean using the 1998 release of the World Ocean Atlas from the Ocean Climate Laboratory at the National Oceanographic Data Center. The data processing is similar to that used by Najjar and Keeling [1997] to create an oxygen climatology. The spatial extrapolation of the nutrients exploits regional relationships between nutrients and temperature in the ocean. The annual mean horizontal and vertical distributions of the nutrients follow the large scale patterns of oceanic circulation as previously reported in the literature. Surface seasonal variations of nutrients are high in the high latitudes and some restricted upwelling areas, whereas in the subtropical oligotrophic gyres nutrients are low all year. Surface seasonal variations are characterized by high values in winter and low values in summer, consistent with the dominance of entrainment during the winter and biological production during the summer. Good agreement is found between the climatologies and the limited reports of seasonal nutrient variations in the literature. Weaker seasonal variations of opposite phasing are found below roughly 100 m and likely reflect the dominance of remineralization during the summer and ventilation during the winter. Spring‐summer export production derived from the seasonal nutrient drawdown in the upper 100 m is 4.2±0.6 Tmol P, 59±8 Tmol N, and 70±15 Tmol Si. The N:P drawdown ratio is, within the error, in agreement with the traditional value of 16. Similarly, the Si:N drawdown ratio is in agreement with the value of 1 expected for diatom growth in unstressed conditions. The export of organic carbon estimated from the phosphate drawdown is 5.3±0.8 Gt C. The shallow remineralization inferred from seasonal phosphate variation between 100 and 200 m is 2.6±1.1 Gt C. The carbon and silica fluxes, considering that they are lower bounds on global export production because they do not capture the production signal in advectively dominated systems, are in reasonable agreement with other large scale estimates of organic carbon and silica export. The computed f ratio (using satellite‐based estimates of primary production) and the ratio of shallow aphotic zone remineralization to new production tend to increase with increasing latitude, supporting an increase in respiration with temperature, as suggested in recent studies.
Automatic Lexical Classification – Balancing between Machine Learning and Linguistics

Verb classifications have been used to support a number of pra ctical tasks and ap- plications, such as parsing, information extraction, ques tion-answering, and machine trans- lation. However, large-scale exploitation of verb classes in real-world or domain-sensitive tasks has not been possible because existing manually built classifications are incomprehen- sive. This paper describes recent and on-going research on extending and acquiring lexical classifications automatically. The automatic approach is a ttractive since it is cost-effective and opens up the opportunity of learning and tuning lexical classifications for the application and domain in question. However, the development of an optimal approach is challenging, and requires not only expertise in machine learning but also a good understanding of the linguistic principles of lexical classification.
Towards a forensically ready cloud storage service

In this paper we examine the feasibility of developing a forensic acquisition tool in a distributed file system. Using GFS as a vehicle and through representative scenarios we develop forensic acquisition processes and examine both the requirements of the tool and the distributed file system must meet in order to facilitate the acquisition. We conclude that cloud storage has features that could be leveraged to perform acquisition (such as redundancy and replication triggers) but also maintains a complexity, which is higher than traditional storage systems leading to a need for forensic-readiness-by-design.
Secure embedded processing through hardware-assisted run-time monitoring

Security is emerging as an important concern in embedded system design. The security of embedded systems is often compromised due to vulnerabilities in "trusted" software that they execute. Security attacks exploit these vulnerabilities to trigger unintended program behavior, such as the leakage of sensitive data or the execution of malicious code. In this work, we present a hardware-assisted paradigm to enhance embedded system security by detecting and preventing unintended program behavior. Specifically, we extract properties of an embedded program through static program analysis, and use them as the bases for enforcing permissible program behavior in real-time as the program executes. We present an architecture for hardware-assisted run-time monitoring, wherein the embedded processor is augmented with a hardware monitor that observes the processor's dynamic execution trace, checks whether the execution trace falls within the allowed program behavior, and flags any deviations from the expected behavior to trigger appropriate response mechanisms. We present properties that can be used to capture permissible program behavior at different levels of granularity within a program, namely inter-procedural control flow, intra-procedural control flow, and instruction stream integrity. We also present a systematic methodology to design application-specific hardware monitors for any given embedded program. We have evaluated the hardware requirements and performance of the proposed architecture for several embedded software benchmarks. Hardware implementations using a commercial design flow, and architectural simulations using the SimpleScalar framework, indicate that the proposed technique can thwart several common software and physical attacks, facilitating secure program execution with minimal overheads.
Soil fertility constraints to small-scale agriculture in North-west Zambia

The soils of north-west Zambia are largely unexplored and are regarded by local residents as problematic in providing sufficient nutrients for the staple crop of maize in the area. The area is semi-tropical, with an average rainfall of 1300mm annum-1 falling predominantly in the summer. The undulating landscape is dominated by miombo woodland interspersed with savanna grassland. Little work has been done on these soils and further information is required to understand their origin and their fertility status. The main objectives of this thesis were: 1) to classify and sample soils from a large number of small-scale agricultural lands, 2) to develop a better understanding of these soils chemical characteristics, 3) to determine the effect of vegetation clearance on soil fertility by sampling adjacent uncultivated land, and 4) to test locally derived rock dust as a soil ameliorant in pot trial. Soils from 100 agricultural and adjacent bush/forest sites were classified and analysed to determine their fertility status. They were tentatively classified according to the WRB system and are dominated by Arenosols, Acrisols and Ferralsols with infrequent occurrences of Lixisols. Most of these soils have a sandy texture. The clay fraction comprises of gibbsite, kaolinite and hydroxyl-Al interlayered vermiculite (HIV), with a few soils also having some mica present. The soils are consistently acidic with 42% of soils sampled having a pH (KCl) <4.3. Furthermore, the subsoils proved to be equally if not more acidic than the topsoils. Low nutrient levels are invariably associated with the soil acidity, with 84% of soils having <15mg/kg P, 59% of soils <50 mg/kg K, 80% soils <300 mg/kg Ca, and 44% soils <80 mg/kg Mg. Comparisons between cultivated and bushland soils showed no consistent changes to the soil acidity and fertility. This is contrary to research that was reviewed but is likely to have been affected by the spatial variability of these soils arising from the termite dominated landscape and the soils having been derived from different parent materials. Amelioration of these soils is required in order to increase yields. It is thought that lime, used with an N, P, K, Zn and B fertilizer, will best remedy the deficiencies found in these soils. Using these nutrients could raise the yields of <1t.ha-1 to in excess of 5 t.ha-1. Local soil ameliorants of crushed rock, ash, compost, green manure and termite mounds were also considered with pulverised granite being tested through factorial pot trials to determine its usefulness as a
Performance Analysis of Phase Gradient Autofocus for Compensating Ionospheric Phase Scintillation in BIOMASS P-Band SAR Data

The P-band synthetic aperture radar of the European Space Agency BIOMASS mission will be affected by ionospheric phase scintillation at high latitudes, which introduces a random high-order azimuth phase error. The dependence of the performance of the phase gradient autofocus (PGA) algorithm for scintillation compensation on the strength of ionospheric turbulence and the signal-to-clutter ratio (SCR) is analyzed. In order to keep resolution degradation below 2%, the SCR must exceed 16 and 20 dB for turbulence strengths CkL = 1033 and 1034, respectively. For large values of CkL, phase scintillation adds significantly to post-PGA degradation in the integrated and peak sidelobe ratios. Simulations based on scenes derived from PALSAR data demonstrate the effectiveness of PGA.
A Chinese OCR spelling check approach based on statistical language models

This work describes an effective spelling check approach for Chinese OCR with a new multi-knowledge based statistical language model. This language model combines the conventional n-gram language model and the new LSA (latent semantic analysis) language model, so both local information (syntax) and global information (semantic) are utilized. Furthermore, Chinese similar characters are used in Viterbi search process to expand the candidate list in order to add more possible correct results. With our approach, the best recognition accuracy rate increases from 79.3% to 91.9%, which means 60.9% error reduction.
Semidefinite relaxation and randomization for dynamic cell association in heterogeneous networks

In this paper, we consider a heterogeneous network with one macro node and one pico node. We are concerned with the problem of associating users in the macro cell, to either the macro node or the pico node, in order to maximize the sum rate in the downlink. We formulate a new theoretical framework to study this problem and derive an upper bound on the achievable sum rate using semidefinite relaxation. Furthermore, we propose a randomized heuristic to produce a feasible solution, and most importantly, give an analytic guarantee on its performance. Independently of the problem data, we can ensure a worst case performance for the randomization method. In practice, this guarantee is as good as the standard best SNR heuristic typically used in 3GPP LTE networks.
Amortized Constant Time State Estimation in SLAM using a Mixed Kalman-Information Filter

The computational bottleneck in all information- based algorithms for SLAM is the recovery of the state mean and covariance. The mean is needed to evaluate model Jacobians and the covariance is needed to generate data association hypotheses. Recovering the state mean and covariance requires the inversion of a matrix of the size of the state. Current state recovery methods use sparse linear algebra tools that have quadratic cost, either in memory or in time. In this paper, we present an approach to state estimation that is worst case linear both in execution time and in memory footprint at loop closure, and constant otherwise. The approach relies on a state representation that combines the Kalman and the information-based state representations. The strategy is valid for any SLAM system that maintains constraints between robot poses at different time slices. This includes both Pose SLAM, the variant of SLAM where only the robot trajectory is estimated, and hierarchical techniques in which submaps are registered with a network of relative geometric constraints.
Acceleration of flowering in Arabidopsis thaliana by Cape Verde Islands alleles of FLOWERING H is dependent on the floral promoter FD

Flowering time in the model plant Arabidopsis thaliana is regulated by both external environmental signals and internal developmental pathways. Natural variation at the FLOWERING H (FLH) locus has previously been described, with alleles present in the Cape Verde Islands accession causing early flowering, particularly after vernalization. The mechanism of FLH-induced early flowering is not understood. Here, the integration of FLH activity into the known flowering time pathways is described using molecular and genetic approaches. The identification of molecular markers that co-segregated with the FLH locus allowed the generation of multiple combinations of FLH alleles with mutations in flowering time genes in different flowering pathways. Combining an early flowering FLH allele with mutations in vernalization pathway genes that regulate FLC expression revealed that FLH appears to act in parallel to FLC. Surprisingly, the early flowering allele of FLH requires the floral integrator FD, but not FT, to accelerate flowering. This suggests a model in which some alleles of FLH are able to affect the FD-dependent activity of the floral activator complex.
Towards Component-Based Software Performance Engineering

Early and rigorous performance analysis of component-based systems is a crucial issue in software engineering to guarantee that the developed components and their assemblies will satisfy their quality requirements. We propose an original approach, called the CB-SPE, for component-based software performance engineering. CB-SPE relies on, and adapts to a CB framework, the concepts and steps of the SPE technology and uses for modeling the standard RT-UML profile, reshaped according to the CB principles. The approach is compositional in that it is applied first at the component layer for achieving parametric performance evaluation of the component in isolation, and then at the application layer for predicting the performance of the assembled components on the actual platform. We also outline the architecture of a tool supporting the automation of the proposed approach, and overview related work.
Multipath Model Selection for UWB Channels

In this paper, we develop an algorithm to extract the parameters of a multipath model of an UWB channel from measurement-simulated data. To this end, we cast the parameter estimation problem into a standard model selection problem and apply the well known minimum description length principle to solve the model selection problem. Numerical results obtained based on the CM1 and CM2 models suggest that the proposed algorithm is effective
Ecological limit functions relating fish community response to hydrologic departures of the ecological flow regime in the Tennessee River basin, United States

Ecological limit functions relating streamflow and aquatic ecosystems remain elusive despite decades of research. We investigated functional relationships between species richness and changes in streamflow characteristics at 662 fish sampling sites in the Tennessee River basin. Our approach included the following: (1) a brief summary of relevant literature on functional relations between fish and streamflow, (2) the development of ecological limit functions that describe the strongest discernible relationships between fish species richness and streamflow characteristics, (3) the evaluation of proposed definitions of hydrologic reference conditions, and (4) an investigation of the internal structures of wedge‐shaped distributions underlying ecological limit functions.