Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer-Aided Classification of Zoom-Endoscopical Images Using Fourier Filters

This paper describes an application of machine learning techniques and evolutionary algorithms to colon cancer diagnosis. We propose an automated classification system for endoscopical images, which is supposed to support physicians in making correct decisions. Classification is done according to the pit-pattern scheme, which defines two/six different classes based on the occurrence of patterns on the mucosa. All discriminative information for classification is obtained by filtering an image's frequency domain. A major part of this paper is devoted to the search for proper frequency filters. An extensive experimental study compares different search strategies and the resulting classification accuracies. We result in a top classification accuracy of 96.9% and 86.8% for the two- and six-classes case, respectively, using a database of 484 zoom-endoscopic images. We observe a tendency toward the employment of lower frequency filter structures for the best classification settings.
An Empirical Model of Acknowledgement for Spoken-Language Systems

We refine and extend prior views of the description, purposes, and contexts-of-use of acknowledgment acts through empirical examination of the use of acknowledgments in task-based conversation. We distinguish three broad classes of acknowledgments (other←ackn, self←other←ackn, and self+ackn) and present a catalogue of 13 patterns within these classes that account for the specific uses of acknowledgment in the corpus.
Functional Mapping of Quantitative Trait Loci That Interact With the hg Mutation to Regulate Growth Trajectories in Mice

The high growth (hg) mutation increases body size in mice by 30–50%. Given the complexity of the genetic regulation of animal growth, it is likely that the effect of this major locus is mediated by other quantitative trait loci (QTL) with smaller effects within a web of gene interactions. In this article, we extend our functional mapping model to characterize modifier QTL that interact with the hg locus during ontogenetic growth. Our model is derived within the maximum-likelihood context, incorporated by mathematical aspects of growth laws and implemented with the EM algorithm. In an F2 population founded by a congenic high growth (HG) line and non-HG line, a highly additive effect due to the hg gene was detected on growth trajectories. Three QTL located on chromosomes 2 and X were identified to trigger significant additive and/or dominant effects on the process of growth. The most significant finding made from our model is that these QTL interact with the hg locus to affect the shapes of the growth process. Our model provides a powerful means for understanding the genetic architecture and regulation of growth rate and body size in mammals.
Can fuzzy logic predict consumer ethnocentric tendencies? An empirical analysis in Jordan

This paper applies fuzzy logic approach to examine consumer ethnocentrism level for Jordanians based on their socio-psychological variables namely, patriotism, nationalism and internationalism. To model the relationships between three inputs namely, patriotism, nationalism and internationalism and one output ethnocentrism MATLAB® ANFIS has been used, utilizing a sample of 341 Jordanians live in Amman city. Results indicate that fuzzy logic model can predict correctly consumers' ethnocentric tendencies knowing their patriotic, nationalistic and internationalist feelings. Fuzzy logic approach shows that high level of nationalism leads consumers to be ethnocentric.
Most Influential Contextual-Features [MICF] based model for Context-Aware Recommender System

Recommender system is an information filtering system that finds its applications in various e-commerce related fields. It recommends a list of items to an end-user from a potentially overwhelming collection of choices. Since the preferences of a user is different from the likings of other users, traditional recommender systems that recommend toprated entities to all the users, may not suffice in anticipating the needs of a user. Therefore, contextualization of recommender system is required to act more efficiently and in a user-specific manner. In an effort to deliver personalized recommendations shaped by user's contextual information, we have devised a novel methodology to incorporate contextual information into the recommender system. The proposed algorithm presents a framework for identifying the relevant contextual-variables and generating the cluster of contextual-features that depict similar rating-pattern for each class of entities. Thereafter, determining the set of Most Influential Contextual-Features that exhibit same rating-pattern as the end-user across all classes and predict the rating an end-user will give to an item, he has not rated before. Our algorithm not only renders intelligent and personalized recommendations but also alleviates cold-start, sparsity and newitem problem of traditional recommender system.
Angiotensin II receptor blockers and cardiovascular protection: Focus on left ventricular hypertrophy regression and atrial fibrillation prevention

Left ventricular hypertrophy (LVH) and atrial fibrillation (AF) are strong predictors of cardiovascular (CV) morbidity and mortality, independently of blood pressure levels and other modifiable and nonmodifiable risk factors. The actions of circulating and tissue angiotensin II, mediated by AT1 receptors, play an important role in the development of a wide spectrum of cardiovascular alterations, including LVH, atrial enlargement and AF. Growing experimental and clinical evidence suggests that antihypertensive drugs may exert different effects on LVH regression and new onset AF in the setting of arterial hypertension. Since a number of large and adequately designed studies have found angiotensin II receptor blockers (ARBs) to be more effective in reducing LVH than beta-blockers and data are also available showing their effectiveness in preventing new or recurrent AF, it is reasonable to consider this class of drugs among first line therapies in patients with hypertension and LVH (a very high risk phenotype predisposing to AF) and as adjunctive therapy to antiarrhythmic agents in patients undergoing pharmacological or electrical cardioversion of AF.
A Swarm Based Defense Technique for Jamming Attacks in Wireless Sensor Networks

Jamming can interrupt wireless transmission and occur by mistake in form of interference, noise or as collision at the receiver or in the circumstance of an attack. In this paper, we propose a swarm based defense technique for jamming attacks in wireless sensor networks. Swarm intelligence algorithm is proficient enough to adapt change in network topology and traffic. The sender and receiver change channels in order to stay away from the jammer, in channel hoping technique. The jammers remain on a single channel, hoping to disrupt any fragment that may be transmitted in the pulse jamming technique. Using the swarm intelligence technique, the forward ants either unicast or broadcast at each node depending on the availability of the channel information for end of the channel. If the channel information is available, the ants randomly choose the next hop. As the backward ants reaches the source, the data collected is verified which channel there is prevalence of attacker long time, and those are omitted. Simultaneously the forward ants are sent through other channels which are not detected before for attacks. This scheme helps limit the channel maintenance overhead. By simulation results, it is clear that this swarm based defense technique for jamming attack is more effective than the existing works.
Dynamic Window-based Adaptive Median Filter Alogorithm

In response to the flaw that the median filtering algorithm has a poor handling capacity to high-density and fine texture noise, a Dynamic window-based adaptive median filter algorithm is proposed. According to the associated level between noise-point information and the surrounding, the new algorithm adjust, the Noise point filter value, which can get a better deal with the details of the images. The adaptive strategies strengthen the performance of the filtering de-noising algorithm, which is good at dealing the high-density noise. Through simulation analysis, the new algorithm is better than other algorithms.
Enhancing Network Lifetime in Wireless Sensor Network using Energy Aware - Lazy Scheduling Algorithm

Wireless Sensor Networks are an emerging technology with potential application in areas as wide ranging as habitat monitoring and industrial applications. In these applications, sensor nodes use batteries as sole energy source result- ing in degradation of energy. Hence, a key issue in wireless sensor network is maximizing network lifetime. A framework to maximize the lifetime of wireless sensor network by using a novel sleep scheduling technique called Virtual Backbone Scheduling (VBS) that enables fine-grained sleep scheduling is proposed. VBS schedules multiple overlapped backbones which work alternatively to prolong network lifetime so that energy consumption is evenly distributed among all the sensor nodes. An approximation algorithms based on Scheduling Transition Graph (STG) is introduced to overcome the scheduling problem of VBS called Maximum Lifetime Backbone Scheduling (MLBS) problem. With the aim of further improving the ef- ficiency and conservative management of energy resources, with automatic ability of foreseeing the task energy starving during run-time an Energy Aware - Lazy Scheduling Algorithm (EA-LSA) is to be proposed.
Maternal tuberculosis: a risk factor for mother-to-child transmission of human immunodeficiency virus.

BACKGROUND Maternal human immunodeficiency virus (HIV) RNA load, CD4 cell count, breast-feeding, antiretroviral use, and malaria are well-established factors associated with mother-to-child transmission (MTCT) of HIV; the role of maternal tuberculosis (TB), however, has not been well established. METHODS The study population was 783 HIV-infected Indian mother-infant pair participants in randomized and ancillary HIV-infected cohorts of the Six Week Extended-Dose Nevirapine (SWEN) Study, a study comparing extended nevirapine versus single-dose nevirapine, to reduce MTCT of HIV among breast-fed infants. Using multivariable logistic regression, we assessed the impact of maternal TB occurring during pregnancy and through 12 months after delivery on risk of MTCT. RESULTS Of 783 mothers, 3 had prevalent TB and 30 had incident TB at 12 months after delivery. Of 33 mothers with TB, 10 (30%) transmitted HIV to their infants in comparison with 87 of 750 mothers without TB (12%; odds ratio [OR], 3.31; 95% confidence interval [CI], 1.53-7.29; P = .02). In multivariable analysis, maternal TB was associated with 2.51-fold (95% CI, 1.05-6.02; P = .04) increased odds of HIV transmission adjusting for maternal factors (viral load, CD4 cell count, and antiretroviral therapy) and infant factors (breast-feeding duration, infant nevirapine administration, gestational age, and birth weight) associated with MTCT of HIV. CONCLUSIONS Maternal TB is associated with increased MTCT of HIV. Prevention of TB among HIV-infected mothers should be a high priority for communities with significant HIV/TB burden.
A Parallel Framework for In-Memory Construction of Term-Partitioned Inverted Indexes

With the advances in cloud computing and huge RAMs provided by 64-bit architectures, it is possible to tackle large problems using memory-based solutions. Construction of term-based, partitioned, parallel inverted indexes is a communication intensive task and suitable for memory-based modeling. In this paper, we provide an efficient parallel framework for in-memory construction of term-based partitioned, inverted indexes. We show that, by utilizing an efficient bucketing scheme, we can eliminate the need for the generation of a global vocabulary. We propose and investigate assignment schemes that can reduce the communication overheads while minimizing the storage and final query processing imbalance. We also present a study on how communication among processors should be carried out with limited communication memory in order to reduce the total inversion time. We present several different communication-memory organizations and discuss their advantages and shortcomings. The conducted experiments indicate promising results.
Sufficient Conditions on Stable Recovery of Sparse Signals With Partial Support Information

In this letter, we study signal reconstruction from compressed sensing measurements. We propose new sufficient conditions for stable recovery when partial support information is available. Weighted l1-minimization is adopted to recover the original signal under three noise models. The proposed approach is to use Ozeki's inequality and shifting inequality in order to bound the errors in the associated weighted l1 -minimization. Our result offers generalized performance bounds on recovery capturing known support information. Improved sufficient conditions for recovery are derived based on our results, even for the cases where the accuracy of prior support information is arbitrarily low.
Adaptive management of watersheds and related resources

The concept of learning about natural resources through the practice of management has been around for several decades and by now is associated with the term adaptive management. The objectives of this paper are to offer a framework for adaptive management that includes an operational definition, a description of conditions in which it can be usefully applied, and a systematic approach to its application. Adaptive decisionmaking is described as iterative, learning-based management in two phases, each with its own mechanisms for feedback and adaptation. The linkages between traditional experimental science and adaptive management are discussed.
Walking pattern generation based on energy function and inverse pendulum model for biped robot

The main propose of this paper is to make comparison between the ZMP-based Gravity-Compensated Inverted Pendulum Model(GCIPM) joint trajectory generator and the unequal height of hip joint trajectory generator proposed in our previous work based on energy efficiency function. Because the battery equipped on biped robot is very height and cannot maintain the power supply status for a very long period, the walking pattern of biped robot needs to be not only human-like but energy-efficient. Our hip trajectory pattern is proved to be able to work with good stability and energy-efficient in our simulation environment. And we also investigate the robot is more energy efficient with the proposed trajectory generator than GCIPM trajectory generator with large step length walking pattern.
Permuting streaming data using RAMs

This article presents a method for constructing hardware structures that perform a fixed permutation on streaming data. The method applies to permutations that can be represented as linear mappings on the bit-level representation of the data locations. This subclass includes many important permutations such as stride permutations (corner turn, perfect shuffle, etc.), the bit reversal, the Hadamard reordering, and the Gray code reordering. The datapath for performing the streaming permutation consists of several independent banks of memory and two interconnection networks. These structures are built for a given streaming width (i.e., number of inputs and outputs per cycle) and operate at full throughput for this streaming width. We provide an algorithm that completely specifies the datapath and control logic given the desired permutation and streaming width. Further, we provide lower bounds on the achievable cost of a solution and show that for an important subclass of permutations our solution is optimal. We apply our algorithm to derive datapaths for several important permutations, including a detailed example that carefully illustrates each aspect of the design process. Lastly, we compare our permutation structures to those of Järvinen et al. [2004], which are specialized for stride permutations.
Extensive molecular dynamics simulations showing that canonical G8 and protonated A38H+ forms are most consistent with crystal structures of hairpin ribozyme.

The hairpin ribozyme is a prominent member of the group of small catalytic RNAs (RNA enzymes or ribozymes) because it does not require metal ions to achieve catalysis. Biochemical and structural data have implicated guanine 8 (G8) and adenine 38 (A38) as catalytic participants in cleavage and ligation catalyzed by the hairpin ribozyme, yet their exact role in catalysis remains disputed. To gain insight into dynamics in the active site of a minimal self-cleaving hairpin ribozyme, we have performed extensive classical, explicit-solvent molecular dynamics (MD) simulations on time scales of 50-150 ns. Starting from the available X-ray crystal structures, we investigated the structural impact of the protonation states of G8 and A38, and the inactivating A-1(2'-methoxy) substitution employed in crystallography. Our simulations reveal that a canonical G8 agrees well with the crystal structures while a deprotonated G8 profoundly distorts the active site. Thus MD simulations do not support a straightforward participation of the deprotonated G8 in catalysis. By comparison, the G8 enol tautomer is structurally well tolerated, causing only local rearrangements in the active site. Furthermore, a protonated A38H(+) is more consistent with the crystallography data than a canonical A38. The simulations thus support the notion that A38H(+) is the dominant form in the crystals, grown at pH 6. In most simulations, the canonical A38 departs from the scissile phosphate and substantially perturbs the structures of the active site and S-turn. Yet, we occasionally also observe formation of a stable A-1(2'-OH). A38(N1) hydrogen bond, which documents the ability of the ribozyme to form this hydrogen bond, consistent with a potential role of A38 as general base catalyst. The presence of this hydrogen bond is, however, incompatible with the expected in-line attack angle necessary for self-cleavage, requiring a rapid transition of the deprotonated 2'-oxyanion to a position more favorable for in-line attack after proton transfer from A-1(2'-OH) to A38(N1). The simulations revealed a potential force field artifact, occasional but irreversible formation of "ladder-like", underwound A-RNA structure in one of the external helices. Although it does not affect the catalytic center of the hairpin ribozyme, further studies are under way to better assess possible influence of such force field behavior on long RNA simulations.
Characterizing users tagging behavior in academic blogs

Along with popular of academic social media, academic blogs are one of the user generated academic information that can be annotated using social tags for user's information retrieval and organization. In order to improve the existing social tagging system to satisfy the users' needs, users' tagging behavior need to be understood. However, there is no researches on characterizing user tagging behaviors of academic resources. In this paper, using the tag of academic blog as the research object, the author analyze user's tagging behaviors based on the characteristics of tags (tags-based features) and those related to blog contents (content-based features). These characteristics can be used to the academic tagging system to promote organization and propagation of academic knowledge.
Fear of crime and health in residential tower blocks. A case study in Liverpool, UK.

BACKGROUND Though it is often assumed that fear of crime erodes mental health, research evidence is limited. Our study seeks to assess the relationship between these attributes in residents of the city of Liverpool. METHOD Evidence is drawn from a sample survey of 407 adults living in 21 tower blocks. A number of social and psychosocial attributes linked with feelings of safety are compared with self-reported health status using logistic and multiple regression techniques. Possible reciprocal relationships were investigated using two-stage least squares. RESULTS Fear of crime in this sample is generally much lower in the home than in Britain as a whole and much higher out on the neighbouring streets at night, but there are sub-group variations. We find significant associations between fear of crime and health status. Feelings of safety when out alone after dark is the most consistent predictor of health status. Those feeling safe score significantly higher on all five dimensions of the SF-36 measure which cover mental and social well-being. Mental health is the strongest correlate and is probably a consequence rather than cause of feelings of safety. CONCLUSION The evidence suggests elderly residents believe tower blocks provide safe accommodation. However, feelings of safety in these 'fortresses' do not generally extend to walking in neighbouring streets. Fear of crime erodes quality of life and is associated with poorer health.
Honeycomb Architecture for Energy Minimum-Cost in Wireless Sensor Networks

The focus of Wireless sensor networks is how to use limited energy to be able to make the network more effectively. Therefore, energy-saving research has becoming a major focus of recent research, and the network topology control is a part of energy-saving research, We propose that the sensor field be overlaid with a honeycomb virtual mesh based on a tessellation. Based on this cellular structure, we transmitted the data in the form of Packet forwarding. And we can save energy to prolong the life time of the Wireless Sensor Networks.
Effect of adductor canal block on pain in patients with severe pain after total knee arthroplasty: a randomized study with individual patient analysis.

BACKGROUND Total knee arthroplasty (TKA) is associated with varying degrees of pain. A considerable proportion (25-40%) of patients experience severe pain, despite a comprehensive multimodal analgesic regimen. We hypothesized that adductor canal block (ACB) would reduce pain in this patient category compared with placebo. METHODS Fifty patients with severe pain, defined as having a visual analogue scale (VAS) pain score of >60 during active flexion of the knee on the first or the second postoperative day after TKA, were included in this randomized, double-blind, placebo-controlled trial. All the patients had received a comprehensive multimodal analgesic regimen. Group A received an ACB with ropivacaine 0.75%, 30 ml at time 0 and isotonic saline after 45 min. Group B received an ACB with isotonic saline at time 0 and ropivacaine 0.75%, 30 ml after 45 min. RESULTS A 32-mm difference in VAS pain score, during active flexion of the knee (primary endpoint), was observed in favour of Group A, 95% confidence interval (CI): 23-42, P<0.0001. At rest, the difference in VAS pain score was 15 mm in favour of Group A, 95% CI: 8-23 mm, P=0.0001. Individual patient analysis revealed that 25% of the patients had no effect during active flexion. At rest, however, only 8% had more than mild pain after ACB compared with 57% at inclusion. CONCLUSIONS ACB reduced VAS with 32 mm, during active flexion of the knee, in patients with severe pain after TKA, but a large proportion (78%) still had at least moderate, movement-related pain. Clinical trial registration www.clinicaltrials. gov, NCT01549704.
Fuzzy Local Discriminant Embedding (FLDE) For Face Recognition

Face images are always affected by variations in illumination conditions and different facial expressions in the real world. Recently, local discriminant embedding (LDE) was proposed to manifold learning and pattern classification. LDE achieves good discriminating performance by integrating the information of neighbor and class relations between data points. But LDE cannot solve illumination problem in face recognition. So, the fuzzy local discriminant embedding (FLDE) algorithm is proposed, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the local distribution information of original samples. Experimental results on ORL, Yale and AR face databases show the effectiveness of the proposed method.