Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing Multi-Field Text Features for Efficient Email Spam Filtering

Abstract Large-scale spam emails cause a serious waste of time and resources. This paper investigates the text features of email documents and the feature noises among multi-field texts, resulting in an observation of a power law distribution of feature strings within each text field. According to the observation, we propose an efficient filtering approach including a compound weight method and a lightweight field text classification algorithm. The compound weight method considers both the historical classifying ability of each field classifier and the classifying contribution of each text field in the current classified email. The lightweight field text classification algorithm straightforwardly calculates the arithmetical average of multiple conditional probabilities predicted from feature strings according to a string-frequency index for labeled emails storing. The string-frequency index structure has a random-sampling-based compressible property owing to the power law distribution and can largely redu.
A digital-to-analog converter based on differential-quad switching

A high-conversion-rate high-resolution oversampling digital-to-analog converter (DAC) for direct digital modulation is addressed in this paper. A new type of switching scheme, called differential-quad switching, is presented. To verify the feasibility of this scheme, essential parts with some auxiliary circuitry for interfacing were fabricated in a 0.8-/spl mu/m CMOS technology. Measured results show that the switching scheme provides 11-b resolution at 100 MSamples/s and 6-b at 1 GSamples/s. The degradation in signal-to-noise ratio is not observed for the variation of the supply voltage down to 1.5 V, which means the proposed scheme is suitable for low-voltage applications.
ECG based biometric authentication using ensemble of features

In this work, the efficacy of various features on electrocardiogram (ECG) based biometric authentication process is thoroughly examined. In particular, the features acquired from temporal analysis, wavelet transformation, power spectral density estimation and QRS-complex detection over ECG signals are considered. These features are employed with two distinct classification algorithms, namely decision tree and Bayes network, specifically for gender, age and identity recognition problems. The biometric authentication framework is evaluated on a benchmark dataset that contains ECG records of 18 healthy people including 5 men, aged 26 to 45, and 13 women, aged 20 to 50. The results of the experimental analysis reveal that if all those features are used in combination rather than individually, better performance is attained for all classifiers in each recognition problem.
When experts agree: using non-affiliated experts to rank popular topics

In response to a query, a search engine returns a ranked list of documents. If the query is about a popular topic (i.e., it matches many documents), then the returned list is usually too long to view fully. Studies show that users usually look at only the top 10 to 20 results. However, we can exploit the fact that the best targets for popular topics are usually linked to by enthusiasts in the same domain. In this paper, we propose a novel ranking scheme for popular topics that places the most authoritative pages on the query topic at the top of the ranking. Our algorithm operates on a special index of "expert documents. " These are a subset of the pages on the WWW identified as directories of links to non-affiliated sources on specific topics. Results are ranked based on the match between the query and relevant descriptive text for hyperlinks on expert pages pointing to a given result page. We present a prototype search engine that implements our ranking scheme and discuss its performance. With a relatively small (2.5 million page) expert index, our algorithm was able to perform comparably on popular queries with the best of the mainstream search engines.
The Use of Carbon Nanotubes to Reinforce 45S5 Bioglass-Based Scaffolds for Tissue Engineering Applications

Bioglass has been used for bone-filling material in bone tissue engineering, but its lean mechanical strength limits its applications in load-bearing positions. Carbon nanotubes (CNTs), with their high aspect ratio and excellent mechanical properties, have the potential to strengthen and toughen bioactive glass material without offsetting its bioactivity. Therefore, in this research, multiwall carbon nanotube (MWCNT)/45S5 Bioglass composite scaffolds have been successfully prepared by means of freeze casting process. 45S5 Bioglass was synthesized by the sol-gel processing method. The obtained material was characterized with X-ray powder diffraction (XRD). The mechanical properties of the scaffolds, such as compression strength and elastic modulus, were measured. Finally, compared with the scaffolds prepared by 100% 45S5 Bioglass powders, the addition of 0.25 wt. % MWCNTs increases the compressive strength and elastic modulus of 45S5 Bioglass scaffolds from 2.08 to 4.56 MPa (a 119% increase) and 111.50 to 266.59 MPa (a 139% increase), respectively.
Neurodraughts: the Role of Representation, Search, Training Regime and Architecture in a Td Draughts Player

NeuroDraughts is a draughts playing program similar in approach to NeuroGammon and NeuroChess Tesauro, 1992, Thrun, 1995]. It uses an artiicial neural network trained by the method of temporal diierence learning to learn by self-play how to play the game of draughts. This paper discusses the relative contribution of board representation, search depth, training regime, architecture and run time parameters to the strength of the TDplayer produced by the system.
Advanced Statistical Machine Learning Methods for the Analysis of Neurophysiologic Data with Medical Application

Transcranial magnetic stimulation procedures use a magnetic field to carry a short-lasting electrical current pulse into the brain, where it stimulates neurons, particularly in superficial regions of the cerebral cortex. It is a powerfull tool to calculate several parameters related to the intracortical excitability and inhibition of the motor cortex. The cortical silent period (CSP), evoked by magnetic stimulation, corresponds to the suppression of muscle activity for a short period after a muscle response to a magnetic stimulation. The duration of the CSP is paramount to assess intracortical inhibition, and it is known to be correlated with the prognosis of stroke patients’ motor ability. Current mechanisms to estimate the duration of the CSP are mostly based on the analysis of raw electromyographical (EMG) signal and they are very sensitive to the presence of noise. This master thesis is devoted to the analysis of the EMG signal of stroke patients under rehabilitation. The use of advanced statistical machine learning techniques that behave robustly in the presence of noise for this analysis allows us to accurately estimate signal parameters such as the CSP. The research reported in this thesis provides us with a first evidence about their applicability in other areas of neuroscience.
Graphical Model-Based Learning in High Dimensional Feature Spaces

Digital media tend to combine text and images to express richer information, especially on image hosting and online shopping websites. This trend presents a challenge in understanding the contents from different forms of information. Features representing visual information are usually sparse in high dimensional space, which makes the learning process intractable. In order to understand text and its related visual information, we present a new graphical model-based approach to discover more meaningful information in rich media. We extend the standard Latent Dirichlet Allocation (LDA) framework to learn in high dimensional feature spaces.
Small Molecules Targeting c-Myc Oncogene: Promising Anti-Cancer Therapeutics

The nuclear transcription factor c-Myc is a member of the Myc gene family with multiple functions and located on band q24.1 of chromosome 8. The c-Myc gene is activated by chromosomal translocation, rearrangement, and amplification. Its encoded protein transduces intracellular signals to the nucleus, resulting in the regulation of cell proliferation, differentiation, and apoptosis, and has the ability to transform cells and bind chromosomal DNA. c-Myc also plays a critical role in malignant transformation. The abnormal over-expression of c-Myc is frequently observed in some tumors, including carcinomas of the breast, colon, and cervix, as well as small-cell lung cancer, osteosarcomas, glioblastomas, and myeloid leukemias, therefore making it a possible target for anticancer therapy. In this minireview, we summarize unique characteristics of c-Myc and therapeutic strategies against cancer using small molecules targeting the oncogene, and discuss the prospects in the development of agents targeting c-Myc, in particular G-quadruplexes formed in c-Myc promoter and c-Myc/Max dimerization. Such information will be of importance for the research and development of c-Myc-targeted drugs.
{36 () Bayesian Network Classiiers. *

Recent work in supervised learning has shown that a surprisingly simple Bayesian classiier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classiiers such as C4.5. This fact raises the question of whether a classiier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classiiers from data, based on the theory of learning Bayesian networks. Bayesian networks are factored representations of probability distributions that generalize the naive Bayesian classiier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which out-performs naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that are characteristic of naive Bayes. We experimentally tested these approaches, using benchmark problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper-based feature selection methods.
License Plate Character Recognition System using Neural Network

Intelligent Transportation System (ITS) has become an integral part of the Transportation Industry these days and it consists of License Plate Recognition (LPR) System. License Plate Recognition is also called Car Plate Recognition (CPR) or Automatic Number Plate Recognition (ANPR) System. In LPR System, when a vehicle steps over magnetic loop detector it senses car and takes image of the car, following image preprocessing operations for improvement in the quality of car image. From this enhanced image, license plate region is recognized and extracted. Then character fragmentation/segmentation is performed on extracted License Plate and these segmented characters are recognized using Neural Network in this paper. General Terms Image Processing, Neural Network.
Sentinel Node Mapping of VX2 Carcinoma in Rabbit Thigh with CT Lymphography Using Ethiodized Oil

Objective To assess the feasibility of computed tomography (CT) lymphography using ethiodized oil for sentinel node mapping in experimentally induced VX2 carcinoma in the rabbit thigh. Materials and Methods This experiment received approval from the institutional animal use and care administrative advisory committee. Twenty-three rabbits with VX2 carcinoma in the thigh underwent CT before and after (1 hour, 2 hour) peritumoral injection of 2 mL ethiodized oil. After the CT examination, sentinel nodes were identified by peritumoral injection of methylene blue and subsequently removed. The retrieved sentinel and non-sentinel lymph nodes were investigated with radiographic and pathologic examinations. Based on the comparison of CT findings with those of radiographic and pathologic examinations, the diagnostic performance of CT for sentinel node identification was assessed. Results All 23 rabbits showed 53 ethiodized oil retention nodes on post-injection CT and specimen radiography, and 52 methylene blue-stained nodes at the right femoroiliac area. Of the 52 blue-stained sentinel nodes, 50 nodes demonstrated ethiodized oil retention. Thus, the sentinel node detection rate of CT was 96% (50 of 52). On pathologic examination, 28 sentinel nodes in 17 rabbits (nodes/rabbit, mean ± standard deviation, 1.7 ± 0.6) harbored metastasis. Twenty seven of the 28 metastatic sentinel nodes were found to have ethiodized oil retention. Conclusion Computed tomography lymphography using ethiodized oil may be feasible for sentinel node mapping in experimentally induced VX2 carcinoma in the rabbit thigh.
Variation of laminar depth in normal eyes with age and race.

PURPOSE To determine if laminar depth (LD) and prelaminar tissue volume (PTV) are associated with age and race in healthy human eyes. METHODS Optic nerve head images from enhanced depth imaging spectral-domain optical coherence tomography of 166 normal eyes from 84 subjects of African descent (AD) and European descent (ED) were manually delineated to identify the principal surfaces: internal limiting membrane, Bruch's membrane (BM), anterior sclera (AS), and anterior surface of the lamina cribrosa. These four surfaces defined the LD and PTV using Bruch's membrane opening (BMO) and AS for reference structures. Generalized estimating equations were used to evaluate whether the effect of age on each outcome was differential by race. RESULTS When age was analyzed as a continuous variable, the interaction term between age and race was statistically significant for mean LDBMO (P = 0.015) and mean LDAS (P = 0.0062) after adjusting for axial length and BMO area. For every 1-year increase in age, the LDAS was greater on average by 1.78 μm in AD subjects and less by 1.71 μm in ED subjects. Mean PTV was lower in the older subjects (1248 × 10(6) μm(3) AD, 881 × 10(6) μm(3) ED) compared to the younger subjects (1316 × 10(6) μm(3) AD, 1102 × 10(6) μm(3) ED) in both groups. CONCLUSIONS With increasing age, the LD changes differently across racial groups in normal subjects. The LD in ED subjects showed a significantly decreasing slope suggesting that the lamina moves anteriorly with age in this group.
Under Consideration for Publication in Theory and Practice of Logic Programming Active Integrity Constraints and Revision Programming

We study active integrity constraints and revision programming, two formalisms designed to describe integrity constraints on databases and to specify policies on preferred ways to enforce them. Unlike other more commonly accepted approaches, these two formalisms attempt to provide a declarative solution to the problem. However, the original semantics of founded repairs for active integrity constraints and justified revisions for revision programs differ. Our main goal is to establish a comprehensive framework of semantics for active integrity constraints, to find a parallel framework for revision programs, and to relate the two. By doing so, we demonstrate that the two formalisms proposed independently of each other and based on different intuitions when viewed within a broader semantic framework turn out to be notational variants of each other. That lends support to the adequacy of the semantics we develop for each of the formalisms as the foundation for a declarative approach to the problem of database update and repair. In the paper we also study computational properties of the semantics we consider and establish results concerned with the concept of the minimality of change and the invariance under the shifting transformation.
Pythia reloaded: an intelligent unit testing-based code grader for education

Automatic assessment of code to support education is an important feature of many programming learning platforms. Unit testing frameworks can be used to perform a systematic functional test of codes; they are mainly used by developers. Competition graders can be used to safely execute code in sandboxed environments; they are mainly used for programming contests. This paper proposes a platform combining the advantages of unit testing and competition graders to provide a unit testing-based grader. The proposed platform assesses codes and produces relevant and "intelligent" feedbacks to support learning. The paper presents the architecture of the platform and how the unit tests are designed.
Evolutionary Conservation of Infection-Induced Cell Death Inhibition among Chlamydiales

Control of host cell death is of paramount importance for the survival and replication of obligate intracellular bacteria. Among these, human pathogenic Chlamydia induces the inhibition of apoptosis in a variety of different host cells by directly interfering with cell death signaling. However, the evolutionary conservation of cell death regulation has not been investigated in the order Chlamydiales, which also includes Chlamydia-like organisms with a broader host spectrum. Here, we investigated the apoptotic response of human cells infected with the Chlamydia-like organism Simkania negevensis (Sn). Simkania infected cells exhibited strong resistance to apoptosis induced by intrinsic stress or by the activation of cell death receptors. Apoptotic signaling was blocked upstream of mitochondria since Bax translocation, Bax and Bak oligomerisation and cytochrome c release were absent in these cells. Infected cells turned on pro-survival pathways like cellular Inhibitor of Apoptosis Protein 2 (cIAP-2) and the Akt/PI3K pathway. Blocking any of these inhibitory pathways sensitized infected host cell towards apoptosis induction, demonstrating their role in infection-induced apoptosis resistance. Our data support the hypothesis of evolutionary conserved signaling pathways to apoptosis resistance as common denominators in the order Chlamydiales.
A personalization recommendation method with time characteristics

Personalization recommendation can effectively solve the negative influence of information overload to users in the environment of big data. And the existing personalization recommendation model is insufficient in integrating the time characteristics of users' behavior. We build a new extend model of personalization recommendation based on the method of latent factor model. Time characteristics of users' historical behavior are introduced into new model to improve the predictions' accuracy. we implement the new model based on the method of factorization machines, and verify the validity of new model by using the data of movielens dataset. Experimental results demonstrate better performance of new model in improving the predictions' accuracy of users' preferences compared with existing model.
Low-Grade Carotid Stenosis: Looking Beyond the Lumen With MRI

Background and Purpose— The management of carotid atherosclerosis is well-established for symptomatic stenosis above 69%, but the optimal approach for managing lower degrees of narrowing remains uncertain. Because the risk of stroke increases with higher grades of stenosis, we are inclined to consider low-grade disease to be low risk. This approach, however, does not take into account other factors such as plaque size or composition. Plaque may progress to a substantial size before it demonstrates significant stenosis by angiography. We know that low-grade disease can result in cerebrovascular ischemic events, but predicting vulnerable lesions has not been possible by relying on stenosis alone. Summary of Review— An understanding of the clinical behavior of plaque causing little to no narrowing is now possible with the advent of high-resolution black blood MRI, a modality that does not rely on luminal narrowing for detection. Conclusion— We present the current understanding of the clinical implications of low-grade carotid stenosis with an example of the MRI assessment of high-risk carotid plaque causing minimal narrowing that highlights the importance of looking beyond the lumen.
Situating conversations within the language/action perspective: the Milan conversation model

The debate on the language/action perspective has been receiving attention in the CSCW field for almost ten years. In this paper, we recall the most relevant issues raised during this debate, and propose a new exploitation of the language/action perspective by considering it from the viewpoint of understanding the complexity of communication within work processes and the situatedness of work practices. On this basis, we have defined a new conversation model, the Milan Conversation Model, and we are designing a new conversation handler to implement it.
Structural investigation on the intrinsically disordered N-terminal region of HPV16 E7 protein

Human papillomavirus (HPV) is the major cause of cervical cancer, a deadly threat to millions of females. The early oncogene product (E7) of the high-risk HPV16 is the primary agent associated with HPV-related cervical cancers. In order to understand how E7 contributes to the transforming activity, we investigated the structural features of the flexible N-terminal region (46 residues) of E7 by carrying out N-15 heteronuclear NMR experiments and replica exchange molecular dynamics simulations. Several NMR parameters as well as simulation ensemble structures indicate that this intrinsically disordered region of E7 contains two transient (10-20% populated) helical pre-structured motifs that overlap with important target binding moieties such as an E2F-mimic motif and a pRb-binding LXCXE segment. Presence of such target-binding motifs in HPV16 E7 provides a reasonable explanation for its promiscuous target-binding behavior associated with its transforming activity. [BMB Reports 2016; 49(8): 431-436]
Railway Embankments as New Habitat for Pollinators in an Agricultural Landscape

Pollinating insect populations, essential for maintaining wild plant diversity and agricultural productivity, rely on (semi)natural habitats. An increasing human population is encroaching upon and deteriorating pollinator habitats. Thus the population persistence of pollinating insects and their associated ecosystem services may depend upon on man-made novel habitats; however, their importance for ecosystem services is barely understood. We tested if man-made infrastructure (railway embankments) in an agricultural landscape establishes novel habitats that support large populations of pollinators (bees, butterflies, hoverflies) when compared to typical habitats for these insects, i.e., semi-natural grasslands. We also identified key environmental factors affecting the species richness and abundance of pollinators on embankments. Species richness and abundance of bees and butterflies were higher for railway embankments than for grasslands. The occurrence of bare (non-vegetated) ground on embankments positively affected bee species richness and abundance, but negatively affected butterfly populations. Species richness and abundance of butterflies positively depended on species richness of native plants on embankments, whereas bee species richness was positively affected by species richness of non-native flowering plants. The density of shrubs on embankments negatively affected the number of bee species and their abundance. Bee and hoverfly species richness were positively related to wood cover in a landscape surrounding embankments. This is the first study showing that railway embankments constitute valuable habitat for the conservation of pollinators in farmland. Specific conservation strategies involving embankments should focus on preventing habitat deterioration due to encroachment of dense shrubs and maintaining grassland vegetation with patches of bare ground.