Miracle-dz/UniFAR-FTUs huggingface dataset:
Test Case Reduction Based on Program Invariant and Genetic Algorithm

Test case reduction is the focus of the field of software testing. In this paper, we integrate random testing methods, invariant technology and genetic algorithms for test case reduction. Program invariant here refers to the properties at some points of program, it can reveal the extent of program data coverage and other valuable information. We evaluate the parent program invariants to filter corresponding set of test cases, and keep the better ones on the offspring. Generation by generation, we can get the final reduced set of test cases. This method is easy to automate. Experimental results and analysis show that compared to similar methods, at the same coverage conditions, the method proposed in this paper have higher efficiency and can get better set of test cases.
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks

The reliance on deep learning algorithms has grown significantly in recent years. Yet, these models are highly vulnerable to adversarial attacks, which introduce visually imperceptible perturbations into testing data to induce misclassifications. The literature has proposed several methods to combat such adversarial attacks, but each method either fails at high perturbation values, requires excessive computing power, or both. This letter proposes a computationally efficient method for defending the Fast Gradient Sign (FGS) adversarial attack by simultaneously denoising and compressing data. Specifically, our proposed defense relies on training a fully connected multi-layer Denoising Autoencoder (DAE) and using its encoder as a defense against the adversarial attack. Our results show that using this dimensionality reduction scheme is not only highly effective in mitigating the effect of the FGS attack in multiple threat models, but it also provides a 2.43x speedup in comparison to defense strategies providing similar robustness against the same attack.
Poor readability of written asthma management plans found in national guidelines.

OBJECTIVE Written asthma management plans (WAMPs) for patients constitute a key component of current national asthma guidelines, but it is not known whether these plans meet the readability standards (of fifth-grade level or lower) recommended by health education experts. The objective of this study was to assess whether WAMPs presented in national guidelines are written at or below a fifth-grade reading level. METHODS We used readability software to analyze 10 WAMPs. These included 7 from the national guidelines, 1 from the World Health Organization, and 2 local ones. RESULTS The grade levels for the WAMPs ranged from 4.9 to 9.2. None of the national plans achieved a grade level equal to or below the fifth grade. However, the other plans had grade levels of 4.9, 5.7, and 5.9. The mean grade level for the national plans was 8.1, whereas the mean for the other 3 plans was 5.5. The difference between these 2 sets of WAMPs was significant. CONCLUSION WAMPs presented as part of the national guidelines are not written at or below a fifth-grade reading level. However, it is clear from this study that it is possible to achieve this level of readability. Increasing the availability and use of plans that meet recognized readability standards may help to improve asthma outcomes, especially in poor populations in which there is both low literacy and the greatest prevalence and severity of asthma.
Optimization and comparison of myocardial T1 techniques at 3T in patients with aortic stenosis

Aims To determine the optimal T1 mapping approach to assess myocardial fibrosis at 3T. Methods and results T1 mapping was performed at 3T using the modified look-locker-inversion sequence in 20 healthy volunteers and 20 patients with aortic stenosis (AS). Pre- and post-contrast myocardial T1, the partition coefficient (λ; ΔRmyocardium/ΔRblood, where ΔR = 1/post-contrast T1 − 1/pre-contrast T1), and extracellular volume fraction [ECV; λ (1 − haematocrit)] were assessed. After establishing the optimal time point and myocardial region for analysis, we compared the reproducibility of these T1 measures and their ability to differentiate asymptomatic patients with AS from healthy volunteers. There was no segmental variation across the ventricle in any of the T1 measures evaluated. λ and ECV did not vary with time, while post-contrast T1 was relatively constant between 15 and 30 min. Thus, mid-cavity myocardium at 20 min was used for subsequent analyses. ECV displayed excellent intra-, inter-observer, and scan–rescan reproducibility [intra-class correlation coefficients (ICC) 1.00, 0.97, and 0.96, respectively], as did λ (ICC 0.99, 0.94, 0.93, respectively). Moreover, ECV and λ were both higher in patients with AS compared with controls (ECV 28.3 ± 1.7 vs. 26.0 ± 1.6%, P < 0.001; λ 0.46 ± 0.03 vs. 0.44 ± 0.03, P = 0.02), with the former offering improved differentiation. In comparison, scan–rescan reproducibilities for pre- and post-contrast myocardial T1 were only modest (ICC 0.72 and 0.56) with no differences in values observed between cases and controls (both P> 0.05). Conclusions ECV appears to be the most promising measure of diffuse myocardial fibrosis at 3T based upon its superior reproducibility and ability to differentiate disease from health.
A visibility matching tone reproduction operator for high dynamic range scenes

We present a tone reproduction operator that preserves visibility in high dynamic range scenes. Our method introduces a new histogram adjustment technique, based on the population of local adaptation luminances in a scene. To match subjective viewing experience, the method incorporates models for human contrast sensitivity, glare, spatial acuity and color sensitivity. We compare our results to previous work and present examples of our techniques applied to lighting simulation and electronic photography.
TR 10017 Modeling Time Varying Covariance Matrices in Low Dimensions

In several modern applications, one considers high dimensional data with time varying covariance matrices. While methods such as Principal Component Analysis (PCA) are well suited for dimensionality reduction of static data, such methods were not designed to find a suitable subspace which can account for the variability in the covariance structure. In this paper, we present a new model which finds a suitable low-dimensional subspace which captures the variations of high-dimensional covariance matrices. While the problem can be posed as one of tensor decomposition, standard approaches to tensor decomposition rely on suitable initialization and may obtain poor local minima on convergence. By analyzing the structure of the problem, we establish lower and upper bounds of the global maximum in terms of a simpler problem. We use the bounds to propose an initialization and an iterative update algorithm with provable approximation guarantees with respect to the global maximum. We also establish conditions under which the method will obtain the global maxima. We illustrate the effectiveness of the proposed method through experiments on synthetic data as well as two real stock market datasets each spanning 14 years where the method finds major financial events in low dimensions.
Candidate Microbicides Block HIV-1 Infection of Human Immature Langerhans Cells within Epithelial Tissue Explants

Initial biologic events that underlie sexual transmission of HIV-1 are poorly understood. To model these events, we exposed human immature Langerhans cells (LCs) within epithelial tissue explants to two primary and two laboratory-adapted HIV-1 isolates. We detected HIV-1Ba-L infection in single LCs that spontaneously emigrated from explants by flow cytometry (median of infected LCs = 0.52%, range = 0.08–4.77%). HIV-1–infected LCs downregulated surface CD4 and CD83, whereas MHC class II, CD80, and CD86 were unchanged. For all HIV-1 strains tested, emigrated LCs were critical in establishing high levels of infection (0.1–1 μg HIV-1 p24 per milliliter) in cocultured autologous or allogeneic T cells. HIV-1Ba-L (an R5 HIV-1 strain) more efficiently infected LC–T cell cocultures when compared with HIV-1IIIB (an X4 HIV-1 strain). Interestingly, pretreatment of explants with either aminooxypentane-RANTES (regulated upon activation, normal T cell expressed and secreted) or cellulose acetate phthalate (potential microbicides) blocked HIV-1 infection of LCs and subsequent T cell infection in a dose-dependent manner. In summary, we document HIV-1 infection in single LCs after exposure to virus within epithelial tissue, demonstrate that relatively low numbers of these cells are capable of inducing high levels of infection in cocultured T cells, and provide a useful explant model for testing of agents designed to block sexual transmission of HIV-1.
Life stressors and resources as predictors of adolescent suicide attempt

The continuing rise in adolescent suicide worldwide constitutes a serious public health challenge. Several environmental and personal factors, such as early losses, discordant relationships, poverty, abuse and other life crises have previously been associated with the rise in adolescent suicides. However, only a few South African studies have investigated the combined occurrence of these factors among the different racial groups. This study investigated the role of gender, race and psychosocial stressors and resources in attempted suicide among 1 033 Grade 11 and 12 learners from schools in the Free State Province, South Africa. A cross-sectional research design was applied. Participants completed a biographical questionnaire and the Life Stressors and Social Resources Inventory, Youth Form. Logistic regression analysis was used to identify stressors, resources and demographic variables that predict attempted suicide among the sample of learners. The findings of the current study suggest that 12.5% of the sample had previously attempted suicide. Being coloured (p ≤ .01) and being female (p ≤ .01) significantly increased the likelihood of attempting suicide. Stressors significantly associated with the increased likelihood of attempting suicide for the whole group were stressors regarding Parents (p ≤ .05), Extended Family (p ≤ .01), Home and Money (p ≤ .05), and Negative Life Events (p ≤ .01). Resources significantly associated with the reduced likelihood of attempting suicide for the whole group were supportive relationships with Parents (p ≤ .01), with Boyfriend/Girlfriend (p ≤ .01) and Positive Life Events (p ≤ .01). These findings highlight the importance of supportive relationships and stable home conditions for the well-being of adolescents. The limitations of this study included an under-representation of black participants in the sample. Furthermore, owing to the cross-sectional design of the study, conclusions cannot be drawn with regard to any causal relationship between demographic variables, life stressors and resources, and attempted suicide. It is recommended that future research studies include factors such as self-esteem and sense of coherence.
Indexing of spatiotemporal Data: A comparison between sweep and z-order space filling curves

Storing constantly changing spatial and temporal (spatio-temporal) features requires multi-dimensional data support in data management applications. That necessitates allocation of large amount of data storage in computer systems. Commonly, relational database management systems (RDBMS) are used for managing those data. However, an algorithm should be implemented to map multi-dimensional data to one-dimensional data in RDBMS. We found that a group of researchers worked on this problem and solved with SPIT (Space-Partitioning with Indexes on Time) approach [Mallett 2004]. SPIT partitions space according to sweep-space filling curve and the researchers argue that using sweep-space filling curve puts lesser demands on I/O than z-order space filling curve. Accordingly, researchers suggest using the former method for storing and indexing of spatio-temporal data. In this paper, we present the results of a series of experiments where we partitioned space by z-order space filling curve and used a sliding window technique for tracking of moving objects. We suggest that in the cases where the tracked objects are close to each other, we can obtain better performance from z-order partitioning than sweep-space filling curve by using sliding window technique for tracking of moving objects.
Efficient Methods for Lifted Inference with Aggregate Factors

Aggregate factors (that is, those based on aggregate functions such as SUM, AVERAGE, AND etc.) in probabilistic relational models can compactly represent dependencies among a large number of relational random variables. However, propositional inference on a factor aggregating n k-valued random variables into an r-valued result random variable is O(r k 2n). Lifted methods can ameliorate this to O(r nk) in general and O(r k log n) for commutative associative aggregators. In this paper, we propose (a) an exact solution constant in n when k = 2 for certain aggregate operations such as AND, OR and SUM, and (b) a close approximation for inference with aggregate factors with time complexity constant in n. This approximate inference involves an analytical solution for some operations when k > 2. The approximation is based on the fact that the typically used aggregate functions can be represented by linear constraints in the standard (k –1)-simplex in Rk where k is the number of possible values for random variables. This includes even aggregate functions that are commutative but not associative (e.g., the MODE operator that chooses the most frequent value). Our algorithm takes polynomial time in k (which is only 2 for binary variables) regardless of r and n, and the error decreases as n increases. Therefore, for most applications (in which a close approximation suffices) our algorithm is a much more efficient solution than existing algorithms. We present experimental results supporting these claims. We also present a (c) third contribution which further optimizes aggregations over multiple groups of random variables with distinct distributions.
Effective Privacy Preservation over Composite Events with Markov Correlations

With the rapid development of radio frequency identification (RFID) and sensor networks, complex event processing (CEP) has attracted extensive attention. Meanwhile, privacy preservation techniques towards composite events are becoming increasingly important. However, practical applications may generate a large number of uncertain data due to inaccurate reading and missing reading. An effective method to model such uncertain data is leveraging the Markov model while conducting CEP over it may cause severe privacy leakage. In this paper, we focus on the privacy preservation on the composite events modeled by Markov chains. Due to the inherent uncertainty and correlations of the data, traditional techniques for privacy preservation fail to support the problem. Specifically, we propose two methods (Type_S and Instance_S) with different optimization goals in processing efficiency and the published result amount. The empirical evaluation verifies the efficiency and applicability of our proposed methods.
New chaotic image encryption technique

This paper presents a new image encryption technique based on neural chaotic generator. This encryption technique includes two main operations, permutation at pixel level and masking and permutation at bit level. The chaotic generator used in the encryption of image is perturbed by a new technique done by artificial neural network. Simulations show that the proposed encryption technique is effective and has a high level security.
SAT based low power scheduling and module binding with clock gating

In today's circuit designs, with increasing density of devices and fast augmentation of clock frequencies, low-power design is a crucial issue. Clock power devours 60-70 percent of aggregate chip power. This is because power is directly proportional to voltage and also the frequency of the clock when the modules are unused. The purpose of this work is to explore the module binding to reduce the clock tree power based on clock gating. Based on the perception that module binding in high level synthesis has a vital impact on the power consumption of the gated clock tree, a satisfiability (SAT) based approach is proposed in this paper which is based on reduction of the activity pattern, especially with functional module binding. The scheduling and module binding is formulated as a satisfiability problem (SAT) and a PB-SAT (pseudo-Boolean) solver is utilized for discovering the optimal binding solution that minimizes the total power consumption because of the activity pattern.
Epigenetic Biomarkers: Potential Applications in Gastrointestinal Cancers

Genetics and epigenetics coregulate the cancer initiation and progression. Epigenetic mechanisms include DNA methylation, histone modification, chromatin remodeling, and noncoding RNAs. Aberrant epigenetic modifications play a fundamental role in the formation of gastrointestinal cancers. Advances in epigenetics offer a better understanding of the carcinogenesis and provide new insights into the discovery of biomarkers for diagnosis, and prognosis prediction of human cancers. This review aims to overview the epigenetic aberrance and the clinical applications as biomarkers in gastrointestinal cancers mainly gastric cancer and colorectal cancer.
Leadership in Software Development: Comparing Perceptions of Agile and Traditional Project Managers

Information systems development has always been a focal point of IS research. The most widely understood traditional methods for developing information systems are based on the “waterfall” life cycle model. Recently, agile methods have drawn increasing attention from IS practitioners and researchers worldwide, as a response to the shortcomings of the traditional methods. However, the impact of agile methods on project management is still not fully understood as empirical studies are scarce. This study uses a full range leadership model as a theoretical lens to analyze project managers’ perceptions of appropriate leadership styles when leading agile vs. traditional software development projects. The findings show that project managers perceive a need for more transformational leadership to achieve success in agile projects as compared to traditional projects.
Thermo-electro-mechanical V-shaped actuator design and simulations

This work details the 3D finite element electro- thermo-mechanical simulation of a current driven V-shaped actuator. The latter consists of two suspended polysilicon arms in a V configuration. The interest of this structure is that it can provide an important linear displacement parallel to the substrate. Stationary and parametric simulations were performed using COMSOL 3.3 and the obtained results were validated experimentally. In addition, a first attempt to model the actuator's ageing behaviour has been made using an empirical model.
A Selective Kernel PCA Algorithm for Anomaly Detection in Hyperspectral Imagery

In this paper, a selective kernel principal component analysis algorithm is proposed for anomaly detection in hyperspectral imagery. The proposed algorithm tries to solve the problem brought by high dimensionality of hyperspectral images in anomaly detection. This algorithm firstly performs kernel principal component analysis (KPCA) on the original data to fully mine high-order correlation between spectral bands. Then, high-order statistics in local scene are exploited to define local average singularity (LAS), which is used to measure the singularity of each nonlinear principal component transformed. Based on LAS, one component transformed with maximum singularity is selected after KPCA. Finally, with RX detector, anomaly detection is performed on the component selected. Numerical experiments are conducted on real hyperspectral images collected by AVIRIS. The results prove that the proposed algorithm outperforms the conventional RX algorithm
HAM proteins promote organ indeterminacy

HAIRY MERISTEM (HAM) proteins, members of the GRAS family of transcriptional regulators, are essential for maintenance of indeterminate growth in flowering plant shoots, loss-of-function ham mutants exhibiting a strikingly novel phenotype of shoot meristem arrest and differentiation. Specific cellular/molecular functions of HAM proteins underlying meristem maintenance are unknown. In this review, I highlight findings from recent analyses of Arabidopsis ham (Atham) loss-of-function phenotypes, including that HAM function limits the generation of clonally-derived meristem layers and that HAM function regulates CLAVATA3 expression. I consider how this new information both refines our understanding of the role of HAM proteins in regulating meristem structure and function, and may also suggest possible downstream HAM protein transcriptional targets. Finally, I note the significant phenotypic overlap between Atham phenotypes, and aintegumenta/anintegumenta-like6 double mutant phenotypes, suggesting meristem regulatory functions common to, and possible genetic interactions between, HAM and AINTEGUMENTA.
Comparison and performance analysis of clustering protocol using sleep&wakeup technique in WSN

In order to achieve data information, the Sensor Node's should form into clusters in wireless sensor network. In Multi-hop sensor network, the Base-Station (sink) is might not be considered. That is why, the hot-spot problem may occur in this network. In this paper, we analyze the sleep&wakeup approach, in order to avoid the hot spot (WLAN) problem, which aims to increase the network lifetime using energy as well as packet delivery ratio. In this technique, C-H (cluster-head) has selected based on energy level & Base-Station distance. Using this technique, we can improve the energy consumption & PDR up to 63%, when compare to that of FCA (fuzzy clustering algorithm) and LEACH (low energy adaptive clustering hierarchy) clustering protocol. Our simulation results are shows that the sleep&wakeup approach is better and energy efficient clustering protocol.
Identifying Key Requirements for a New Product Line

Product lines should be based on the business reality in the selected market segments. Since the success of the product line is determined by the success of the resulting products, it is essential to guarantee that the key requirements of each identified market segment are satisfied. A critical issue for any company pursuing product line development is the competitiveness of their products against products from other manufacturers. The competitiveness can be realized by carefully managing the differentiating requirements during the initial analysis of the product line requirements. For industrial scale product lines, the number of initial requirements can be large, seriously hindering the analysis. In this paper, we first discuss the economic foundation of differentiating requirements. Then we show how to identify the key requirements for a product line and in which way we can reduce the set of requirements that needs to be discussed during the analysis. Finally, we demonstrate our approach with a worked example where we analyze the key requirements in terms of differentiation among the products.