Miracle-dz/UniFAR-FTUs huggingface dataset:
New Strategy for Mitigating of SQL Injection Attack

SQL injection attack (SQLIA) is a serious threat to web applications. A successful SQLIAs can have serious consequences to the victimized organization that include financial lose, reputation lose, compliance and regulatory breach. Therefore, developing approaches for mitigating SQLIA is paramount important. To this end, we propose an approach based on negative tainting along with SQL keyword analysis for detecting and preventing SQLIA. We have tested our proposed approach on all types of SQLIAs techniques by generating SQL queries containing legitimate SQL commands and SQLIA. We present an analysis and evaluation of the proposed approach to demonstrate its effectiveness in detecting and protecting SQLIA attack.
NOL11, Implicated in the Pathogenesis of North American Indian Childhood Cirrhosis, Is Required for Pre-rRNA Transcription and Processing

The R565W mutation in the C‐terminus of the nucleolar ribosome biogenesis factor, hUTP4/Cirhin, causes North American Indian childhood cirrhosis (NAIC). Our objective was to identify non‐conserved interaction partners for hUTP4/Cirhin, as a defective protein‐protein interaction may underlie NAIC. By screening a yeast two‐hybrid cDNA library derived from human liver, and through affinity purification followed by mass spectrometry, we identified an uncharacterized nucleolar protein, NOL11, as an interaction partner for hUTP4/Cirhin. Co‐immunoprecipitation experiments show that NOL11 is a component of the nucleolar human ribosomal small subunit (SSU) processome. siRNA knockdown of NOL11 revealed that it is required to generate the mature 18S rRNA and for optimal rDNA transcription. Yeast two‐hybrid analysis shows that NOL11 interacts with the C‐terminus of hUTP4/Cirhin, and that the R565W mutation partially disrupts this interaction. Expression of the hUTP4/Cirhin R565W mutation only partially rescues the ribosome biogenesis defects, indicating that the disease mutation confers a defect in ribosome biogenesis. We have therefore identified NOL11 as a novel protein required for the nucleolar stages of ribosome biogenesis in humans. Our results further implicate a role for NOL11 in the pathogenesis of NAIC. Supported by NIH GM52581.
Reconnecting Digital Publications to the Web using their Spatial Information

Digital publications can be packaged and viewed via the Open Web Platform using the EPUB 3 format. Meanwhile, the increased amount of mobile clients and the advent of HTML5's Geolocation have opened a whole range of possibilities for digital publications to interact with their readers. However, EPUB 3 files often remain closed silos of information, no longer linked with the rest of the Web. In this paper, we propose a solution to reconnect digital publication with the (Semantic) Web. We will also show how we can use that connection to improve contextualization for a user, specifically via spatial information. We enrich digital publications by connecting the detected concepts to their URIs on, e.g., DBpedia, and by devising an algorithm to approximate the location of any detected concept, we can provide a user with the spatial center of gravity of his reading position. The evaluation of the location approximation algorithm showed a high recall, and the high correlation between estimation error and standard deviation can provide the user with a sense of correctness (or spread) of an approximation. This means relevant locations (and their possible radius) can be shown for a user, based on the content he or she is reading, and based on his or her location. This methodology can be used to reconnect digital publications with the online world, to entice readers, and ultimately, as a novel location-based recommendation technique.
Computational Methods for the Study of Face Perception

Our research develops computational approaches for studying face perception, and in particular emotion recognition. We have developed biologically inspired algorithms for representing and detecting the deformable face shape, and defined eye-tracking methodology to infer which aspects of gaze are relevant to a category learning and discrimination task. The first problem, shape detection, is addressed using two different approaches of manifold learning and probabilistic graphical models. In the manifold learning approach, nonlinear regression is used to learn the function relating the object image to the associated shape. This technique is useful because it does not require shape key points be defined at high contrast image regions, nor does it require an initial shape estimate. In addition, the manifold can be learned at extremely coarse resolutions. The probabilistic graph approach uses a graph to encode the homogenous structure of many natural objects. This approach is useful because the graph allows inference of dozens of landmarks and can be applied to a variety of object types. To understand category learning from eye tracking, we have developed tools which identify the variables of an eye tracking sequence which predict if a human subject has learned how to categorize an object. The methodology we developed was applied to both adult and infant subjects, and validated several variables previously used in categorization studies. The final portion of our work involves eye tracking
Faster Valuation of Financial Derivatives

High-dimensional integrals are usually solved with Monte Carlo algorithms although theory suggests that low-discrepancy algorithms are sometimes superior. We report on numerical testing which compares low-discrepancy and Monte Carlo algorithms on the evaluation of financial derivatives. The testing is performed on a Collateralized Mortgage Obligation (CMO) which is formulated as the computation of ten integrals of dimension up to 360. We tested two low-discrepancy algorithms (Sobol and Halton) and two randomized algorithms (classical Monte Carlo and Monte Carlo combined with antithetic variables). We conclude that for this CMO the Sobol algorithm is always superior to the other algorithms. We believe that it will be advantageous to use the Sobol algorithm for many other types of financial derivatives. Our conclusion regarding the superiority of the Sobol algorithm also holds when a rather small number of sample points are used, an important case in practice. We have built a software system called FINDER for computing high-dimensional integrals. FINDER runs on a heterogeneous network of workstations under PVM 3.2 (Parallel Virtual Machine). Since workstations are ubiquitous, this is a cost-effect way to do large computations fast. The measured speedup is at least . 9N for $N$ workstations, $N$ less than or equal to 25. The software can also be used to compute high-dimensional integrals on a single workstation. A routine for generating Sobol points may be found, for example, in "Numerical Recipes in C" by Press et al. However, we incorporated major improvements in FINDER and we stress that the results reported in this paper were obtained using FINDER. One of the improvements was developing the table of primitive polynomials and initial direction numbers for dimensions up to 360.
A Locally Adaptive Background Density Estimator: An Evolution for RX-Based Anomaly Detectors

We propose a local anomaly detection strategy for multi-hyperspectral images in which the background probability density function is estimated with a kernel density estimator and locally adaptive information extracted from the image is injected into the bandwidth selection process. Results for multispectral images of different scenarios show the benefits of the proposed strategy regarding its effectiveness both at detecting anomalies and at avoiding the crucial issue of properly selecting the kernel-width parameter.
Performance of screening mammography in organized programs in Canada in 1996. The Database Management Subcommittee to the National Committee for the Canadian Breast Cancer Screening Initiative.

BACKGROUND The results of randomized trials show that breast cancer screening by mammography reduces breast cancer mortality by up to 40% in women aged 50-69 years. Because of these results, by 1998, 22 countries, including Canada, had established population-based organized screening programs. This paper presents the results of screening mammography in 1996 for 7 provincially organized breast cancer screening programs in Canada. METHODS Analyses of interim performance indicators for screening mammography have been calculated from data submitted to the Canadian Breast Cancer Screening database. The data set consisted of data from 7 provincial programs and was limited to mammographic screens for women aged 50-69 years (n = 203,303). Screening outcomes and performance indicators were calculated for abnormalities detected by screening mammography only. RESULTS The abnormal recall rate was 9.5% for first screens and 4.6% for subsequent screens, and the cancer detection rate per 1000 women screened was 6.9 for first screens and 3.8 for subsequent screens. The positive predictive value (i.e., the proportion of women who tested positive by mammography who were found to have breast cancer on screen-initiated diagnostic work-up) increased from 7.2% at the first screen to 8.2% at subsequent screens. Estimated participation rates within organized programs varied from 10.6% to 54.2%, depending on the province. INTERPRETATION For 1996, organized breast cancer screening programs met or exceeded many of the interim measures used in international programs. It is possible to translate the benefits of breast cancer screening by mammography, as demonstrated in randomized trials, into population-based community programs. Screening mammography through organized programs should increase to allow more comprehensive monitoring in Canada.
Network protocol identification ensemble with EA optimization

In computer networks, the ability to correctly classify and control traffic flows is essential in order to manage network resources. A number of works have focused on the identification of flow attributes, or discriminators, able to distinguish the underlying application protocol of a flow at an early stage of it's existence. In this study k-means is investigated for identifying distinct application protocols present within flow data sets generated using a select number of discriminators. The clusters identified were used in a supervised training process that correctly identified protocols with an almost perfect (99% percent) success rate.
Editable graphical histories

The authors have designed a testbed system that creates a series of automatically generated panels that depict in chronological order the important events in the history of a users session with Chimera, a graphical editor. The authors' system heuristically determines the contents of each panel and the actions that it illustrates. The user can scroll through the sequence of panels, reviewing actions at different levels of detail, and selectively undoing, modifying, and redoing previous actions. <<ETX>>
The use of software as a service by students in higher education institutions: a systematic literature review

In this paper, we provide an overview of the concepts and structure of cloud computing and then focus on Software as a Service (SaaS) because it is the most common cloud computing service model adopted by higher education institutions (HEIs) [29]. SaaS is a service model of cloud computing that has the potential to provide students with improved academic experience by providing them with anytime anywhere access to learning, communication and collaboration materials [28]. It has the potential to turn around the delivery and support of educational services [26]. In this paper, we review the literature on the use of SaaS by students in HEIs. A total of ninety eight recent articles on cloud computing/ SaaS in Higher Education were reviewed and twenty six of the articles were found to be relevant to this paper. This research used a systematic literature review by ensuring that the search terms included all the concerned areas of using cloud computing/ SaaS in HEIs. The contents of online electronic databases including Google Scholar, Elsevier Science direct, Springer Link and IEEE were searched for recent articles and a content analysis of all the identified papers was conducted. The aim was to identify the current state of student's use of SaaS in HEIs. To achieve this, the researchers conducted a systematic review of the relevant papers. This paper also explains SaaS in details and identified the benefits of SaaS as well as the perceived obstacles to student's use of SaaS in HEIs.
Supplementation with High Doses of Vitamin D to Subjects without Vitamin D Deficiency May Have Negative Effects: Pooled Data from Four Intervention Trials in Tromsø

Data were pooled from four randomized clinical trials with vitamin D performed in Tromsø with weight reduction, insulin sensitivity, bone density, and depression scores as endpoints. Serum lipids, glycated hemoglobin (HbA1c), and high sensitivity C-Reactive Protein, (HS-CRP) were measured at baseline and after 6–12 months of supplementation with vitamin D 20 000 IU–40 000 IU per week versus placebo. A total of 928 subjects who completed the interventions were included. At baseline the mean serum 25-hydroxyvitamin D (25(OH)D) level in those given vitamin D was 55.9 (20.9) nmol/L and the mean increase was 82.4 (40.1) nmol/L. Compared with the placebo group there was in the vitamin D group at the end of the studies a slight, but significant, increase in HbA1c of 0.04%, an increase in HS-CRP of 0.07 mg/L in those with serum 25(OH)D < 50 nmol/L, and in those with low baseline HDL-C and serum 25(OH)D < 50 nmol/L a slight decrease serum HDL-C of 0.08 mmol/L (P < 0.05). No serious side-effects were seen. In conclusion, in subjects without vitamin D deficiency, there is no improvement in serum lipids, HbA1c, or HS-CRP with high dose vitamin D supplementation. If anything, the effect is negative.
Simulation and experiment research of equipment parameters on gas-particle two-phase swirling flows finishing

For precise hole surfaces, the burr severely affects products' performance. Considering the specific conditions of the precise hole surface finishing, a new method of gas-particle two-phase swirling flows finishing is brought up. By theoretic analysis, equipment parameters, nozzle angle, nozzle diameter, nozzle quantity, nozzle displacement, etc., affect finishing quality and efficiency. Numerical simulation and experiment research of equipment parameters are done, which provides credible basis for thorough research and equipment design.
Effects of antibody, drug and linker on the preclinical and clinical toxicities of antibody-drug conjugates

ABSTRACT Antibody-drug conjugates (ADCs) represent a new class of cancer therapeutics. Their design involves a tumor-specific antibody, a linker and a cytotoxic payload. They were designed to allow specific targeting of highly potent cytotoxic agents to tumor cells whilst sparing normal cells. Frequent toxicities that may be driven by any of the components of an ADC have been reported. There are currently more than 50 ADCs in active clinical development, and a further ∼20 that have been discontinued. For this review, the reported toxicities of ADCs were analysed, and the mechanisms for their effects are explored in detail. Methods to reduce toxicities, including dosing strategies and drug design, are discussed. The toxicities reported for active and discontinued drugs are important to drive the rational design and improve the therapeutic index of ADCs of the future.
Do Incentive-Based Programs Improve Teacher Quality and Student Achievement? An Analysis of Implementation in 12 Urban Charter Schools

Context Policymakers have increasingly advocated for incentive-based approaches for improving urban schools. Purpose of the Study Few studies have examined the implementation of incentive based approaches in the urban charter school context. This paper presents research findings from a 4-year longitudinal study of the implementation of a comprehensive incentive-based program for school improvement. Setting The study was conducted in cluster of 12 charter schools in a large urban school district. Participants/Subjects Participants in the study included classroom teachers, administrators, and staff in the charter schools; program staff from the school district; and consultants to the project. Research Design The study used a mixed methods design, which included a comparative case study, and quasi-experimental design. Data Collection and Analysis Qualitative data elements included semistructured interviews and observations of program activities. Quantitative data elements included the official classroom teaching evaluation scores, teacher attitudes survey data, standardized student achievement scores from state and district assessment programs, and researcher-developed surveys of teacher and administrator perceptions. Results The results suggest that, in addition to questions about the viability of this comprehensive incentive model, there were several challenges related specifically to the context of the schools that affected program implementation. These included: misconceptions about performance pay; difficulties learning, understanding and sustaining a complex professional development model; poor fidelity of program implementation; varying capacities of the leadership teams; inability to sustain the incentive component; differences in the contexts and missions of the participating schools; and high teacher turnover at the schools. All the 12 schools reported some improvement in student achievement which led to performance rewards; however, this was not substantial or consistent over the course of the 4 years of implementation. The program also did not impact teacher retention at the schools. Schools that benefited the most from the program demonstrated an alignment of goals between the program staff, school leadership, and teachers. Conclusions/Recommendations The study findings suggest that even comprehensive incentive-based models are not yet viable and have limited effectiveness. Educators needed significant salary increases to be incentivized by money and incentive-based models need to be simpler in design for participants to understand them. The aspect that was somewhat sustained was the professional development and collegiality among the teachers indicating that a professional community is what teachers appreciated most from the program.
Comparison of SSVEP Signal Classification Techniques Using SVM and ANN Models for BCI Applications

In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attentions. This study tries to develop a classifier, which can provide higher classification accuracy for multiclass SSVEP data. Four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 second window and features were extracted using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass Support Vector Machines (SVM) is compared with Artificial Neural Network (ANN) models on the basis of SSVEP classifier accuracies. OAA SVM classifier had got an average accuracy of 88.55% for SSVEP classification over 10 subjects. Based on this study, it is found that for SSVEP classification OAA -SVM classifier can provide better results
The Acceptance of E-banking by Customers in Nigeria

The internet is perhaps one of the most useful tools to businesses and individuals in contemporary world economies. Its use has touched virtually every aspect of human endeavour including banking. Technological breakthroughs and product designs have led to the emergence of e-banking services which, in recent time has become globally popular except in developing countries including Nigeria. The Central Bank of Nigeria (CBN) recently released a circular on the introduction of ‘cashless’ policy which sets cash deposit and withdrawal limits. The success of this policy requires the increased use of alternative payment systems including e-banking. However, despite the growing popularity of e-banking in the world, its rate of adoption in Nigeria has been relatively slow. It has therefore become imperative to study the underlying determinants of acceptance e-banking in order to understand and remove the inhibitions surrounding its use and enhance its popularity. The study takes a cross sectional analysis of determinants of acceptance of ebanking in Nigeria using a modified Technology Acceptance Model (TAM) as research framework. Four hundred questionnaires were distributed customers of different banks to elicit relevant data out of which two hundred and forty nine (249) were found to be useful. These questionnaires were designed using the 5point Likert scale and the Cronbach Coefficient Alpha was used to test for reliability and consistency of research instrument. Linear Multiple Regression Analysis was employed to determine the effect of Age (A), Educational Background (PB), Income (Y), Perceived Benefits (PB), Perceived Ease of Use (PEOU), Perceived Risk (PR) and Perceived Enjoyment (PE) on Acceptance of E-banking (AI). The Statistical Package for Social Sciences (SPSS) was used for computation. The result shows that acceptance of e-banking in Nigeria is significantly influenced by Age, Educational Background, Income, Perceived Benefits, Perceived Ease of Use, Perceived Risk and Perceived Enjoyment.
Thermal Infrared Face Recognition – A Biometric Identification Technique for Robust Security system

Face of an individual is a biometric trait that can be used in computer-based automatic security system for identification or authentication of that individual. While recognizing a face through a machine, the main challenge is to accurately match the input human face with the face image of the same person already stored in the face-database of the system. Not only the computer scientists, but the neuroscientists and psychologists are also taking their interests in the field of development and improvement of face recognition. Numerous applications of it relate mainly to the field of security. Having so many applications of this interesting area, there are challenges as well as pros and cons of the systems. Face image of a subject is the basic input of any face recognition system. Face images may be of different types like visual, thermal, sketch and fused images. A face recognition system suffers from some typical problems. Say for example, visual images result in poor performance with illumination variations, such as indoor and outdoor lighting conditions, low lighting, poses, aging, disguise etc. So, the main aim is to tackle all these problems to give an accurate automatic face recognition. These problems can be solved using thermal images and also using fused images of visual and thermal images. The image produced by employing fusion method provides the combined information of both the visual and thermal images and thus provides more detailed and reliable information which helps in constructing more efficient face recognition system. Objective of this chapter is to introduce the role of different IR spectrums, their applications, some interesting critical observations, available thermal databases, review works, some experimental results on thermal faces as well as on fused faces of visual and thermal face images in face recognition field; and finally sorting their limitations out.