Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Technique of Email Classification for Spam Detection

Email spam is one of the unsolved problems of the today's Internet, annoying individual users and bringing financial damage to companies. Among the approaches developed to stop spam emails, filtering is a popular and important one. Common uses for email filters include organizing incoming email and computer viruses and removal of spam. As spammers periodically find new ways to bypass spam filters and distribute spam messages, researchers need to stay on the forefront of this problem to help reduce the amount of spam messages. Currently spam emails occupy more than 70% of all email traffic. The negative effect spam has on companies is greatly related to financial aspects and the productivity of employees in the workplace. In this paper, we propose the new approach to classify spam emails using support vector machine. It found that the SVM outperformed than other classifiers.
The Influence of Internal Stresses on the Microbond Test II: Physical Aging and Adhesion

The present work investigated the effects of physical aging on interfacial adhesion and internal stresses using single-fibre model composites. First, the stress state of the interface and its evolution upon aging were studied for the microbond geometry. This stress analysis was used with experiments to determine an intrinsic interfacial shear strength. Second, this stress analysis, including aging effects, was used in a fracture mechanics or energy release rate analysis of the microbond test, which involves the energy necessary to propagate a crack instead of the minimum stress to initiate a crack. These methods were tested for both freshly cured and aged epoxy droplets on glass fibres, and were shown to yield similar results. It was found that neither the interfacial shear strength nor the interfacial toughness were changed by physical aging of the polymer matrix when the relaxation of internal stresses and shift of relaxation times of the matrix due to physical aging were accounted for. Thus, the intrinsic shear strength and toughness of the fibre– matrix interface were determined.
Computing with faulty arrays

We present and <italic>O</italic>(1) slowdown emulation of a fault-free <italic>N</italic> x <italic>N</italic> two dimensional mesh with a slack of <italic>O</italic>(log <italic>N</italic> log log <italic>N</italic>) by a faulty mesh of the same size and slack. All components of the faulty mesh, including the memory modules, are assumed to be subject to failure. The faults may occur at any time during the emulation and the system readjusts dynamically.
Simple and accurate feature selection for hierarchical categorisation

Categorisation of digital documents is useful for organisation and retrieval. While document categories can be a set of unstructured category labels, some document categories are hierarchically structured. This paper investigates automatic hierarchical categorisation and, specifically, the role of features in the development of more effective categorisers. We show that a good hierarchical machine learning-based categoriser can be developed using small numbers of features from pre-categorised training documents. Overall, we show that by using a few terms, categorisation accuracy can be improved substantially: unstructured leaf level categorisation can be improved by up to 8.6%, while top-down hierarchical categorisation accuracy can be improved by up to 12%. In addition, unlike other feature selection models --- which typically require different feature selection parameters for categories at different hierarchical levels --- our technique works equally well for all categories in a hierarchical structure. We conclude that, in general, more accurate hierarchical categorisation is possible by using our simple feature selection technique.
Corpus Annotation : Challenges and Strategies

Annotation is an important task for Natural Language Processing (NLP), and the traditional annotation schema, including writing detailed guidelines and training annotators, has proved to work well in many previous annotation projects. However, making medical judgment on clinical data requires medical expertise and annotation can only be done by experts. Recently, we created three corpora for our clinical NLP studies: one marks critical recommendations in radiology reports, and the other two indicate whether a patient has pneumonia based on chest X-ray reports or ICU reports. All the annotations were done by medical experts. In this paper, we discuss various challenges we have encountered when dealing with expert annotation, and lay out some lessons we have learned from the annotation tasks. Our experiments show that medical training alone is not sufficient for achieving high inter-annotator agreement, and NLP researchers should get involved in the annotation process as early as possible despite their lack of medical training.
Framing the Immigration Policy Agenda

This study applies a qualitative comparative analysis (QCA) design to examine how configurations of quantitative and qualitative aspects of media coverage associate with changes on the policy agenda. We analyze media coverage of sixteen focusing events related to Dutch immigration policies—an intractable policy controversy that is regularly under media scrutiny. In addition to the quantity of media attention, we take into account whether dominant framing in media coverage is contesting the current policy frame and whether the framing in the media is consonant. Our analysis indicates that frame contestation is a necessary condition for media effects. Quantity of media attention and frame consonance are relevant indicators of changes on the policy agenda only when the majority of media coverage is contesting the current policy frame. Furthermore, we found that in the case of intractable policy controversies, media framing can create specific dynamics, such as “David versus Goliath” dynamics where human-interest framing of a single case challenges current policy, or “negotiation dynamics” where competing managerialist frames negotiate policy solutions. An integration of framing and agenda-setting literatures helps develop a better understanding of the occurrence of media effects on the policy agenda and how this effect takes shape in the case of intractable policy controversies.
The American Association of Suicidology Self-Mutilation and Coping Strategies in a College Sample

The goal of this study was to examine the use of specific coping strategies among self-mutilating college students. The self-mutilating group (n = 44) reported utilizing avoidance strategies more often than did a control group (n = 44) matched for general psychological distress but with no history of self-mutilation. In addition, female, but not male, self-mutilators endorsed using problem-solving and social support seeking strategies less often than nonmutilators. These findings suggest that coping strategies in general and avoidance-based strategies in particular may be important targets for the treatment of self-mutilative behaviors.
College, Career, and Lifestyle Development with Students Who Are LGBQQ: Strategies for School Counselors

Abstract There is a dearth of existing literature pertaining to college, career, and lifestyle development, which can assist school counselors in their work with lesbian, gay, bisexual, queer, and questioning (LGBQQ) students in these areas. Using the work adjustment theory and the Association for Lesbian, Gay, Bisexual, & Transgender Issues in Counseling's Competencies for Counseling Lesbian, Gay, Bisexual, Queer Questioning, Intersex, and Ally Individuals, school counselors can prepare LGBQQ students for the world of college and/or work by reflecting on workplace satisfaction, advocating for inclusive workplace practices, and exploring values within and obstacles to career and lifestyle success. This article offers a discussion of LGBQQ-inclusive strategies for school counselors, including mindsets, core curriculum planning, responsive services, and student assessment.
High-Throughput Multi-Core LDPC Decoders Based on x86 Processor

Low-Density Parity-Check (LDPC) codes are an efficient way to correct transmission errors in digital communication systems. Although initially targeting strictly to ASICs due to computation complexity, LDPC decoders have been recently ported to multicore and many-core systems. Most works focused on taking advantage of GPU devices. In this paper, we propose an alternative solution based on a layered OMS/NMS LDPC decoding algorithm that can be efficiently implemented on a multi-core device using Single Instruction Multiple Data (SIMD) and Single Program Multiple Data (SPMD) programming models. Several experimentations were performed on a x86 processor target. Throughputs up to 170 Mbps were achieved on a single core of an INTEL Core i7 processor when executing 20 layered-based decoding iterations. Throughputs reaches up to 560 Mbps on four INTEL Core-i7 cores. Experimentation results show that the proposed implementations achieved similar BER correction performance than previous works. Moreover, much higher throughputs have been achieved by comparison with all previous GPU and CPU works. They range from x1.4 to x8 by comparison with recent GPU works.
PFBIK-tracking: Particle filter with bio-inspired keypoints tracking

In this paper, we propose a robust detection and tracking method for 3D objects by using keypoint information in a particle filter. Our method consists of three distinct steps: Segmentation, Tracking Initialization and Tracking. The segmentation is made in order to remove all the background information, in order to reduce the number of points for further processing. In the initialization, we use a keypoint detector with biological inspiration. The information of the object that we want to follow is given by the extracted keypoints. The particle filter does the tracking of the keypoints, so with that we can predict where the keypoints will be in the next frame. In a recognition system, one of the problems is the computational cost of keypoint detectors with this we intend to solve this problem. The experiments with PFBIK-Tracking method are done indoors in an office/home environment, where personal robots are expected to operate. The Tracking Error evaluate the stability of the general tracking method. We also quantitatively evaluate this method using a “Tracking Error”. Our evaluation is done by the computation of the keypoint and particle centroid. Comparing our system with the tracking method which exists in the Point Cloud Library, we archive better results, with a much smaller number of points and computational time. Our method is faster and more robust to occlusion when compared to the OpenniTracker.
Pragmatic two-level coded modulation using Reed-Solomon product codes

A pragmatic two-level coded modulation scheme based on the direct product of two high-rate single-error- correcting Reed-Solomon (RS) codes is introduced for bandwidth- efficient transmission in power-limited systems. The proposed scheme uses a single code/decoder. Data rate flexibility is achieved by simply varying the number of uncoded bits. A simple two- stage decoding procedure is described that can operate either on hard- or soft-decisions from the demodulator. A study is carried out in order to optimize the bit-labeling strategy and the minimum Euclidean distance of the code. Simulation results show that the proposed coded modulation scheme exhibits a constant gap to capacity regardless of the cardinality of the signal set. Furthermore this gap can be reduced by considering RS component codes defined over large Galois fields.
Strong ETH holds for regular resolution

We obtain asymptotically sharper lower bounds on resolution complexity for k-CNF's than was known previously. We show that for any large enough k there are k-CNF's which require resolution width (1-~O(k<sup>-1/4</sup>))n, regular resolution size 2<sup>(1-~O(k<sup>-1/4</sup>))n</sup>, and general resolution size (3/2)<sup>(1-~O(k<sup>-1/4</sup>))n</sup>.
Pram Memory Allocation and Initialization

Two techniques for managing memory on a parallel random access machine (PRAM) are presented. One is a scheme for an n/log n processor EREW PRAM that dynamically allocates and deallocates up to n records of the same size in O(log n) time. The other is a simulation of a PRAM with initialized memory by one with uninitialized memory. A CREW PRAM variant of the technique justifies the assumption that memory can be assumed to be appropriately initialized with no asymptotic increase in time but a factor of n increase in space. An EREW PRAM solution incurs a factor of O(log n) increase in time but only a constant factor increase in space.
Extension of Multiresolution Analysis and the Construction of Orthogonal Multiwavelets

Abstract A procedure of the extension of orthogonal multiresolution analysis is introduced by assuming that 1(𝑥) ia an orthogonal uniscaling function and constructing a new orthogonal multiscaling function (𝑥) 1(𝑥), 2(𝑥)𝑇. Moreover, an explicit formula of the orthogonal multiwavelets associated with (𝑥) is obtained. Finally, a construction example is given.
A Sequential Monte Carlo Sampling Approach for Cell Population Deconvolution from Microarray Data

Microarray analyses assume that gene expressions are measured from synchronous, homogeneous cell populations. In reality, the measured gene expression is produced by a mixture of populations in different stages of the cellular life cycle. Hence, it is important to estimate the proportions of cells in different stages and to incorporate this information in the analysis of gene expression, clusters, or function. In this paper we propose a novel unsupervised learning approach that models biological processes, such as the cell-cycle, as a hidden state-space model and uses a particle-filter based approach for estimating model parameters. Our approach finds a maximum a posteriori (MAP) estimate of the cell proportions given the gene expression and an estimate of the stage dependant gene expression. Evaluation of statistical validity of our approach using randomized data tests reveals that our model captures true temporal dynamics of the data. We have applied our approach to model the yeast cell-cycle and extracted profiles of the population dynamics for different stages of the cell-cycle. Our results are in agreement with biological knowledge and reproducible in multiple runs of our algorithm, suggesting that our approach is capable of extracting biologically meaningful and statistically significant information. Finally, the stage dependant gene expression can be used to determine clusters of active genes on a per-stage basis.
Virtual Learning Environments' Impact on Adult Learners' Motivation in the Workplace

Virtual Learning Environments’ Impact on Adult Learners’ Motivation in the Workplace by Clarence E. Bashshar MS, Chestnut Hill College, 2005 MSA, Central Michigan University, 1993 BS, University of Maryland, 1990 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Education Walden University February 2017
A Unifying Omnidirectional Camera Model and its Applications

Omnidirectional cameras are leaving the scientific labs to be used in market applications. Many such application areas like robotics and automotive need very accurate models of the image formation. When a car or robot motion is controlled by an omnidirectional camera system, the exact control of action depends on a reliable calibration. An unprecise calibration can cause costly or hazardous consequences for man and machine. Therefore, we developed an omnidirectional camera model that can deal with parabolic and hyperbolic mirrors in combination with distorting lenses. It has the adequate complexity to allow both, precision and robustness of the calibration process. We successfully applied this model to our driver assistance systems of future light and heavy trucks.
Empirically Guided Software Guesstimation

The state of the art in software project estimation does not deserve this reputation, but the fact remains that modern methods are time-consuming and complex. Estimation methods such as COCOMO II [3] are oriented toward the needs of large and very large software engineering projects, and may require time-consuming calibration to fit the peculiarities of an organization, development team, and application domain. On the other hand, methods such as the Personal Software Process [8] have demonstrated good results in improving small/individual project estimation accuracy, but advocates promote the need for specialized training and adherence to a highly constrained approach to development. Although these approaches have demonstrated success in organizations with the resources necessary to adopt them, the process overhead involved in
SMT Spatio-Temporal Planning

Solving real planning problems requires to consider spatial and temporal information. Indeed, to be solved more efficiently many real world problems need to take the action duration, the instants of effects occurrences, the instants of requiring preconditions. and the space in which the mission is accomplished by defining the different actions zones and know the path between these zones into account. In this paper we are interested in planning problems which consider spatial and temporal dimensions. To our knowledge there is no model expressing both spatial and temporal dimensions in planning problems. The main contribution of this paper is to present an approach allowing the resolution of spatiotemporal planning problems. For this purpose we define a new SMT (Sat Modulo Theory) encoding rules for spatio-temporal planning. This new code can compile and solve spatio-temporal planning problems for which all solutions require simultaneous actions in a 2D space.
Community-aware ranking algorithms for expert identification in question-answer forums

Question-Answer forums (QAF) are significant platforms for disseminating informal information and play important role in problem solving and learning. Expert identification still has some limitations and link analysis methods do not consider community dimension. In this paper an authority analysis approach for identifying experts is proposed. This approach combines overlapping community detection (OCD) algorithms with ranking methods to compute the nodes' expertise level in QAFs. Firstly, graph resulting from a specific search query is computed and an OCD algorithm is applied on it. After identifying clusters of nodes, we change updating rules of original Hyperlink-Induced Topic Search (HITS) and PageRank to take the effect of intra cluster links and extra cluster connections. People whom are intra or overlapping to a community possess higher vision about context of the community than nodes which are outside. We experimented the proposed overlapping community-aware ranking algorithms and compared them with baseline approaches on online forums. Results indicate that OCD improves expert identification accuracy and relevancy.
The Current Waist Circumference Cut Point Used for the Diagnosis of Metabolic Syndrome in Sub-Saharan African Women Is Not Appropriate

The waist circumference cut point for diagnosing the metabolic syndrome in sub-Saharan African subjects is based on that obtained from studies in European populations. The aim of this study was to measure the prevalence of obesity and related metabolic disorders in an urban population of African females, a group at high risk for such diseases, and to determine the appropriate waist cut point for diagnosing the metabolic syndrome. Anthropometry and fasting lipid, glucose and insulin levels were measured in a cohort of 1251 African females participating in the Birth to Twenty cohort study in Soweto, Johannesburg. The waist circumference cut points for diagnosing metabolic syndrome (as defined using the new harmonised guidelines), insulin resistance, dysglycaemia, hypertension and dyslipidaemia were obtained using receiver operator characteristic curve analysis. The prevalence of obesity, type 2 diabetes and metabolic syndrome were 50.1%, 14.3% and 42.1%, respectively. The appropriate waist cut point for diagnosing metabolic syndrome was found to be 91.5 cm and was similar to the cuts points obtained for detecting increased risk of insulin resistance (89.0 cm), dysglycaemia (88.4 cm), hypertension (90.1 cm), hypo-high density lipoproteinaemia (87.6 cm) and hyper-low density lipoproteinaemia (90.5 cm). The present data demonstrates that urban, African females have a high prevalence of obesity and related disorders and the waist cut point currently recommended for the diagnosis of the metabolic syndrome (80.0 cm) in this population should be increased to 91.5 cm. This latter finding demonstrates a clear ethnic difference in the relationship between abdominal adiposity and metabolic disease risk. The similar waist cut points identified for the detection of the individual components of the metabolic syndrome and related cardiovascular risk factors demonstrates that the risk for different metabolic diseases increases at the same level of abdominal adiposity suggesting a common aetiological pathway.
Desire to Be Ethical or Ability to Self‐Control: Which is More Crucial for Ethical Behavior?

Promoting ethical decisions and behaviors is challenging for any organization. Yet managers are still required to make ethical decisions under conditions which deplete their self‐control resources, such as high stress and long hours. This study examines the relationships among symbolic and internal moral identity, self‐control, and ethical behavior, and investigates whether self‐control acts as the mechanism through which moral identity leads to ethical behavior. Findings indicate that internal moral identity overrides symbolic moral identity in the relationship with self‐control and that self‐control fully mediates the relationship between internal moral identity and ethical behavior. The implications for organizations is that while rules, procedures, and ethics training are useful, managers with a strong moral compass will be more likely to practice self‐control leading to more ethical behaviors.