Miracle-dz/UniFAR-FTUs huggingface dataset:
UserRec: A User Recommendation Framework in Social Tagging Systems

Social tagging systems have emerged as an effective way for users to annotate and share objects on the Web. However, with the growth of social tagging systems, users are easily overwhelmed by the large amount of data and it is very difficult for users to dig out information that he/she is interested in. Though the tagging system has provided interest-based social network features to enable the user to keep track of other users' tagging activities, there is still no automatic and effective way for the user to discover other users with common interests. In this paper, we propose a User Recommendation (UserRec) framework for user interest modeling and interest-based user recommendation, aiming to boost information sharing among users with similar interests. Our work brings three major contributions to the research community: (1) we propose a tag-graph based community detection method to model the users' personal interests, which are further represented by discrete topic distributions; (2) the similarity values between users' topic distributions are measured by Kullback-Leibler divergence (KL-divergence), and the similarity values are further used to perform interest-based user recommendation; and (3) by analyzing users' roles in a tagging system, we find users' roles in a tagging system are similar to Web pages in the Internet. Experiments on tagging dataset of Web pages (Yahoo! ~Delicious) show that UserRec outperforms other state-of-the-art recommender system approaches.
Automatically Performing Weak Mutation with the Aid of : Symbolic Execution , Concolic and Search Based Testing

Automating software testing activities can increase the quality and drastically decrease the cost of software development. Towards this direction various automated test data generation tools have been developed. The majority of existing tools aim at structural testing, while a quite limited number aim at a higher level of testing thoroughness such as mutation. In this paper an attempt towards automating the generation of mutation based test cases by utilizing existing automated tools is proposed. This is achieved by reducing the killing mutants’ problem into a covering branches one. To this extent this paper is motivated by the use of state of the art techniques and tools suitable for covering program branches when performing mutation. Tools and techniques, such as symbolic execution, concolic execution and evolutionary testing can be easily adopted towards automating the test input generation activity for the weak mutation testing criterion by simply utilizing a special form of the mutant schemata technique. The propositions made in this paper integrate three automated tools in order to illustrate and examine the method’s feasibility and effectiveness. The obtained results, based on a set of Java program units, indicate the applicability and effectiveness of the suggested technique. The results advocate that the proposed approach is able to guide existing automating tools in producing test cases according to the weak mutation testing criterion. Additionally, experimental results with the proposed mutation testing regime show that weak mutation is able to speedup the mutant execution time by at least 4.79 times when compared with strong mutation.
Endobronchial ultrasonography with guide-sheath for peripheral pulmonary lesions

The usefulness of endobronchial ultrasonography (EBUS) with guide-sheath (GS) as a guide for transbronchial biopsy (TBB) for diagnosing peripheral pulmonary lesions (PPL)s and for improving diagnostic accuracy was evaluated in this study. EBUS-GS-guided TBB was performed in 24 patients with 24 PPLs of ≤30 mm in diameter (average diameter=18.4 mm). A 20-MHz radial-type ultrasound probe, covered with GS was inserted via a working bronchoscope channel and advanced to the PPL in order to produce an EBUS image. The probe with the GS was confirmed to reach the lesion by EBUS imaging and X-ray fluoroscopy. When the lesion was not identified on the EBUS image, the probe was removed and a curette was used to lead the GS to the lesion. After localising the lesion, the probe was removed, and TBB and bronchial brushing were performed via the GS. Nineteen peripheral lesions (79.2%) were visualised by EBUS. All patients whose PPLs were visible on EBUS images subsequently underwent an EBUS-GS-guided diagnostic procedure. A total of 14 lesions (58.3%) were diagnosed. Even when restricted to PPLs <20 mm in diameter, the diagnostic sensitivity was 53%. In conclusion, endobronchial ultrasonography with guide sheath-guided transbronchial biopsy was feasible and effective for diagnosing peripheral pulmonary lesions.
Isomorphism classes of certain complete intersections

An access control system, comprises at least one door to a secured area, each door having a strike plate, a host computer. The access control system further comprises at least one electro-mechanical key to independently actuate a lock that corresponds to the door(s). The host system records information selected from the group consisting of time of entry, place of entry, identification of entered party, and/or any combination thereof. In addition, a door knob and mechanical locking mechanism selectively latches and unlatches the locking mechanism and can be actuated with a mechanical key; and circuitry to actuate the locking mechanism to selectively latch so that the door can open, the circuitry actuated by an electrical signal transmitted by an electrical key, the electrical signal communicated by an electrical contact extending through the mechanical locking mechanism. The circuitry is powered by a battery, which can be removed without disturbing or actuating unlocking mechanism. A master rekeying device comprises input/output circuitry to receive and transmit electrical signals, circuitry coupled to the input/output circuitry to record a list of security passwords in order to check passwords against the list and a memory to store data; and the input/output circuitry also receives and transmits electrical signals to a host computer.
Early supernormal retinal oxygenation response in patients with diabetes.

PURPOSE To determine whether the human retinal oxygenation response (deltaPO2) to a hyperoxic provocation is abnormal in patients with type I diabetes. METHODS Magnetic resonance imaging (MRI) was used to measure deltaPO2 during 100% oxygen breathing in patients with type I diabetes who had either no clinically detectable retinopathy (n = 5) or mild to moderate background diabetic retinopathy (BDR; n = 5) and in age-matched healthy control subjects (n = 7). RESULTS Both the patients with diabetes and the control subjects exhibited a significant (P < 0.05) increase in the preretinal vitreous signal intensity on changing from room air breathing to oxygen inhalation (i.e., 5 minutes). However, only diabetic patients demonstrated significant (P < 0.05) increases in deltaPO2 between measurements made at 5 minutes of oxygen inhalation and measurements at longer durations of hyperoxia (15, 25, and 35 minutes). Furthermore, deltaPO2 was significantly (P < 0.05) greater in patients with diabetes than in control subjects, but there was no significant difference in deltaPO2 (P > 0.05) between patients with diabetes, with or without retinopathy. Age and deltaPO2 correlated significantly (P < 0.05) in control subjects but not in patients with diabetes. In control subjects, deltaPO2 was relatively uniform panretinally, whereas in the diabetic group, changes in oxygenation response were spatially inhomogeneous. CONCLUSIONS These results demonstrate, for the first time, that MRI deltaPO2 detects a significant supernormal retinal oxygenation response in patients with type I diabetes, even before the appearance of retinopathy. This study raises the possibility of using MRI measurements of deltaPO2 to monitor therapeutic efficacy in human trials.
Ubiquitination Regulates the Morphogenesis and Function of Sperm Organelles

It is now understood that protein ubiquitination has diverse cellular functions in eukaryotes. The molecular mechanism and physiological significance of ubiquitin-mediated processes have been extensively studied in yeast, Drosophila and mammalian somatic cells. Moreover, an increasing number of studies have emphasized the importance of ubiquitination in spermatogenesis and fertilization. The dysfunction of various ubiquitin systems results in impaired sperm development with abnormal organelle morphology and function, which in turn is highly associated with male infertility. This review will focus on the emerging roles of ubiquitination in biogenesis, function and stability of sperm organelles in mammals.
Intention to Revisit Traditional Folk Events: A Case Study of Qinhuai Lantern Festival, China

The present study aims at analyzing revisit intention of traditional folk events based on the Theory of Planned Behavior and a Consumer-based Model of Authenticity. The survey data were collected by a questionnaire survey in Nanjing Qinhuai Lantern Festival 2012, China. Structural equation modeling was employed to test the proposed research model. The result shows that perceived behavioral control, motivation and perceived food authenticity of the event, but not attitude and perceived overall authenticity of the event, are valid predictor constructs for visitors' intention to revisit Qinhuai Lantern Festival. The findings have implications for the promotion of traditional events and festival tourism. Copyright © 2013 John Wiley & Sons, Ltd.
Evolving Artificial Neural Networks through L-system and evolutionary computation

The aim of this paper is to present a biologically inspired Neuro Evolutive Algorithm (NEA) able to generate modular, hierarchical and recurrent neural structures as those often found in the nervous system of live beings, and that enable them to solve intricate survival problems. In our approach we consider that a nervous system design and organization is a constructive process carried out by genetic information encoded in DNA. Our NEA evolves Artificial Neural Networks (ANNs) using a Lindenmayer System with memory that implements the principles of organization, modularity, repetition (multiple use of the same sub-structure), hierarchy (recursive composition of sub-structures), as a metaphor for development of neurons and its connections. In our method, this basic neural codification is integrated to a Genetic Algorithm (GA) that implements the constructive approach found in the evolutionary process, making it closest to the biological ones. Our method was initially tested on a simple, but non-trivial, XOR problem. We also submit our method to two other problems of increasing complexity: Prediction of the effect of a new drug on Breast Cancer and KDDCUP'99 benchmark intrusion detection dataset. Some advantages of the proposed methodology are that it increases the level of implicit parallelism of the GA and seems to be capable of generating minimal satisfactory neural architectures, resulting in a reduction of project costs and increasing the performance of the evolved ANN, suggesting a promising potential for future applications.
Non-rigid registration using AAM and factorization method for augmented reality applications

This paper presents a novel non-rigid registration method for augmented reality applications using AAM and factorization method. The method can be divided into two stages: offline construction of 3D shape basis and online estimation of the 3D pose parameters together with the 3D shape coefficients. In offline stage, we get the training data with the use of the AAM algorithm, then we use factorization method to obtain the object 3D shape basis set from image sequences. In online stage, given an initial 3D shape coefficients and the rotation matrix, we can get the non-rigid object coordinates under the world coordinate systems by projecting the 3D points which are the linear combination of the 3D shape basis to the 2D image. Then we use Levenberg-Marquardt algorithm to optimize the 3D shape coefficients and the 3D pose parameters simultaneously. Some experimental results demonstrate this method is effective and useful for augmented reality applications.
A cavity detection method based on machine vision

In order to search and rescue the victims in rubble effectively, a 3D model of the cavity is required. This paper presents a cavity detection method based on machine vision for disaster rescue robot. Laser ring projection technique is developed to lighten the profile of cross-section of the cavity and draw a line around the internal surface. A CMOS camera is used to capture the images which consist of essential profile information as well as various noises. The method of image acquiring and image preprocessing is also described. However, due to the inevitable influence of the harsh environment and lighting conditions, the profile in the acquired image might be partitioned into several segments. Thus, a method of detecting the curve segments based on vector argument is proposed. The 3D model of the cavity is reconstructed by using a series of complete and accurate cross-section profiles which are fitted by the improved cubic B-spline interpolation. Experimental results show that the proposed profile tracking algorithm is effective and adaptable.
Pharmacokinetic parameters of meloxicam after its oral administration in goat

Aim: The objective of the present study was to find out the levels of analgesic drug meloxicam in the blood plasma of young goats. The drug was given to them through oral route. Data was used to elucidate the Pharmacokinetic determinants of the drug which were employed to arrive at the dose schedule and frequency of the drug in goats. Materials and Methods: Elaborate pharmacokinetic research of the drug meloxicam was done on 18 to 24 months old, five adult male local goats (Capra hircus) of Assam weighing 20 to 25 kg.The drug was given orally at the dose rate of 0.35 mg/kg at the Goat Rearing farm, Guwahati, Assam. Analysis of blood was done by high performance liquid chromatography (HPLC) system. Results: The mean values of area under curve (AUC) and mean area under curve (AUMC) were 3137.488 ± 125.3749 2 µg. min/ml and 4650460 ± 380892.4744 µg. min /ml respectively . The mean peak plasma level of meloxicam was 1.972 ± 0.0477 µg/ml at 600 min. The mean values of elimination half life (t ) and absorption half life (t ) were 693±0.00 min and 1/2‚ 1/2Ka 170.6 ± 17.0076 min respectively. The mean values of volume of distribution (Vd) and mean residence time (MRT) were 0.114 ± 0.0156 L/kg and 1472.264 ± 63.336 min respectively. The mean value of T was found to be 497 ± 19.8040 min. Following max single oral administration the minimum effective therapeutic concentration or minimum effective plasma concentration of meloxicam was detectable up to 1200 min. The bioavailibity (F) of the drug was 80.5 ± 10.0150%. Conclusion: These pharmacokinetic determinants were used to determine the frequency and dose schedule of meloxicam in goats. The minimum effective concentration of the drug is 0.7 µg/ml in plasma. To maintain this, an initial loading dose of 0.5 mg/kg body weight should be followed by a maintenance dose of 0.4 mg/kg body weight/10 hour.
Linearly Constrained Minimum Variance Source Localization and Spectral Estimation

A signal's spectrum is a representation of the signal in terms of elementary basis functions which facilitates the extraction of desired information. For a temporal signal, the spectrum is one-dimensional and expresses the time-domain signal as a linear combination of sinusoidal basis functions. A space-time signal possesses a multidimensional Fourier transform known as the wavenumber-frequency spectrum, which represents the space-time signal as a weighted summation of monochromatic plane waves. The spatial and temporal frequencies are not separable, as spatial frequency is itself a function of the temporal frequency. Thus, it seems natural to analyze and estimate the spatial and temporal frequency components in tandem. It is therefore surprising that conventional spectral estimation methods focus on either the spatial or temporal dimension, without any regard for the other. Spatial spectral estimation is commonly referred to as source localization, as the direction of the wavenumber vector is indeed the direction of propagation. Conventional methods analyze a solely spatial aperture without accounting for the temporal structure of the desired signal. Conversely, temporal spectral estimation is performed using a single sensor, and thus the signal aperture is purely temporal. This paper proposes a spatiotemporal framework for spectral estimation based on the linearly constrained minimum variance (LCMV) beamforming method proposed by Frost in 1972. The aperture consists of an array of sensors, each storing a set of previous temporal samples. It is first shown that by taking into account the temporal structure of the desired signal, the ensuing source location estimate is more robust to the effects of noise and reverberation. Unlike conventional localizers, the LCMV steered beam temporally focuses the array onto the desired signal. The desired signal is modeled by an autoregressive (AR) process, and the resulting AR coefficients are embedded in the linear constraints. As a result, the rate of anomalous estimates is significantly reduced as compared to existing techniques. Moreover, it is then demonstrated that by employing multiple sensors and steering the array to the assumed source location, the estimate of the desired signal's temporal spectrum contains a lesser contribution from the unwanted noise and reverberation.
Non-uniform light field compression: a geometry-based method for image processing

A geometry-based image compression method-non-uniform light field compression is proposed by combining bi-triangle-based surface light field partition and non-uniform factoring of light field matrix, through which a four orders magnitude compression ratio can be achieved. Starting from dense images captured from vantage points, the codec resample these original data and partition the resampled data over bi-triangle. Then the codec arrange these partitioned light field data into 2D matrices and approximate the matrices through factoring them into non-uniform textures. Finally the codec group these textures into tiles and further compress these textures using ordinary still image compression techniques. Novel image can be rendered in real-time through texture mapping. In this paper we illustrate the compression efficiency and rendering performance through dense images captured in real scene.
Two-dimensional (recursive) channel equalization for multicarrier systems with soft impulse shaping (MCSIS)

Multicarrier (MC) systems are attractive for the use in mobile radio environments, because they can handle long echoes better than single carrier (SC) systems. To be able to optimally exploit the diversity immanent in the transmission channels a multicarrier system with soft impulse shaping (MCSIS) has been introduced in previous works. This system, that in itself carries intersymbol and adjacent channel interference (ISI and ACI), has so far been implemented with the suboptimal solution of a one-dimensional equalization in either time or frequency direction only. This paper now investigates the possibilities of using MCSIS with two-dimensional equalization structures, either implemented as a combination of two one-dimensional equalizers (pseudo two-dimensional equalization) or implemented with a real two-dimensional equalizer.
3 Statistical learning

Language is a uniquely human endowment – no other animal communicates using a system as rich or inventive as human language. Statistical learning approaches to language emphasize the richness of the human communication: it is the primary source of data from which the child identifies the patterns in their native language. Statistical learning refers to the process of identifying units in the input, such as words or categories, by discovering what features of the input predict other features, and grouping features that are likely to co-occur. It is a domain general ability, meaning that learners can discover these statistical relations in many different types of input, including language, music, vision and other sensory modalities (Fiser & Aslin 2001, Saffran et al. 1997). Humans, from infancy to adulthood, and several species of animals show evidence of statistical learning, suggesting that the mechanism that gives rise to statistical learning is both evolutionarily old and present from – or near to – birth (Kirkham et al. 2002, Toro & Trobalon 2005). This presents a challenge for theories of language that emphasize learning: if animals and adults are capable of statistical learning, why do infants learn language more successfully than any animal, and most adults (e.g. Johnson & Newport 1989)? To begin to answer this question, it is necessary to understand what statistics learners can detect, how the characteristics of the learning mechanism and the learner affect learning, and how these characteristics change with age. The definition of statistical learning – the process of using likelihood of occurrence to group elements in the environment – is in some ways similar to the definition of associative learning. Association is clearly an important component of statistical learning, which requires the ability to associate two stimuli that are likely to co-occur. But the two kinds of learning are not identical; there are many examples of associative learning
THE URBAN BIAS IN RESEARCH ON CIVIL WARS

BENJAMIN A. VALENTINO’S Final Solutions: Mass Killing and Genocide in the 20th Century is an important step toward a better understanding of a vexing and tremendously important phenomenon: mass political violence. Using clear logic and a wealth of secondary materials, Valentino advances a number of important insights and arguments. The book’s central thesis is that there is a strategic logic to mass violence: it is “a brutal strategy designed to accomplish leaders’ most important ideological objectives and counter what they see as their most dangerous threats. ” Mass political violence requires very low levels of social support and tends to be produced by very small groups of people, while most people are uninvolved; they are an unaware public at best, and passive spectators at worst. Valentino distinguishes three types of mass killing: communist, ethnic, and counter-guerrilla. In this paper, I will focus on the third category, which I understand to include cases of ethnically motivated insurgencies that do not result in genocide. I will do so to advance a methodological argument, one that has implications for Valentino’s work but that also covers studies of civil war and political violence in general. To explain why some counterinsurgency campaigns turn highly violent, whereas others do not, Valentino looks at the level of threat posed by an insurgency. Strong and popular guerrilla movements, he argues, pose dangerous and credible threats to governments and, therefore, invite highly violent reprisals. Valentino then supplies two case studies of high threat/high violence (Guatemala and Afghanistan) and several vignettes of counterinsurgency campaigns that did not result in massive murder of civilians. These case studies are based on available secondary studies.