Miracle-dz/UniFAR-FTUs huggingface dataset:
Follow You from Your Photos

In this work, we focus on the travelling location prediction problem of detecting whether a person will leave his living area and where he will go by analyzing the hidden connection between the user behaviors on geography and online social interactions. By analyzing more than 40, 000 Instagram media records from 26, 000 users, spanning a period of 3 months, we give special consideration to rarely visits locations, which are often ignored as noise in previous works, and we employ the dynamic Bayesian network to estimate the users' behavior and predict the location according to a majority voting model based on the social interaction information. We compare our model on the data of Instagram with two existing location prediction models, and find that (1) our model performs well both in the general location prediction and the location outside the living area. (2) social ties are effective for solving the location prediction problem as the accuracy of the prediction gets higher, given more social interaction information.
Bayesian Proportional Hazard Analysis of the Timing of High School Dropout Decisions

In this paper, I study the timing of high school dropout decisions using data from High School and Beyond. I propose a Bayesian proportional hazard analysis framework that takes into account the specification of piecewise constant baseline hazard, the time-varying covariate of dropout eligibility, and individual, school, and state level random effects in the dropout hazard. I find that students who have reached their state compulsory school attendance ages are more likely to drop out of high school than those who have not reached compulsory school attendance ages. Regarding the school quality effects, a student is more likely to drop out of high school if the school she attends is associated with a higher pupil–teacher ratio or lower district expenditure per pupil. An interesting finding of the paper that comes along with the empirical results is that failure to account for the time-varying heterogeneity in the hazard, in this application, results in upward biases in the duration dependence estimates. Moreover, these upward biases are comparable in magnitude to the well-known downward biases in the duration dependence estimates when the modeling of the time-invariant heterogeneity in the hazard is absent.
A Density-Based Energy-Effective Routing for Sensor Networks

Considering the character of very high density of nodes in sensor network, a novel routing algorithm is proposed based on density-controlled and the structure of grid. The algorithm constructs the suitable grid. Then, the density of nodes is controlled to the appropriate range. The redundant nodes will stop working and sleep until the right time to wake up to replace died working nodes. Theoretic analysis and simulation results show that the algorithm has better performances
Close Binary Interactions of Intermediate-Mass Black Holes: Possible Ultraluminous X-Ray Sources?

While many observed ultraluminous X-ray sources (ULXs; LX ≥ 1039 ergs s-1) could be extragalactic X-ray binaries (XRBs) emitting close to the Eddington limit, the highest luminosity ULXs (LX > 3 × 1039 ergs s-1) exceed the isotropic Eddington luminosity for even high-stellar-mass-accreting black hole XRBs. It has been suggested that these highest luminosity ULXs may contain accreting intermediate-mass black hole (IMBH) binaries. We consider this hypothesis for dense, young (~100 Myr) stellar clusters where we assume that a 50-500 M☉ central IMBH has formed through runaway growth of a massive star. We develop numerical simulations of such clusters' cores by combining single and binary star evolutionary syntheses with a simple treatment of dynamical interactions. We model interactions of the IMBH with single and binary stars, as well as single-binary and binary-binary interactions, but not the formation of a cusp around the IMBH. The core density and velocity dispersion are assumed to be constant over 100 Myr. We investigate the succession of IMBH binary companions and the evolution of their orbital parameters to obtain estimates of the incidence of mass transfer phases and possible ULX activity involving the IMBH. We find that although it is common for the central black hole to acquire binary companions, there is a very low probability that these interacting binaries will become observable ULX sources.
OdeView: the graphical interface to Ode

OdeView is the graphical front end for Ode, an object-oriented database system and environment. Ode's data model supports data encapsulation, type inheritance, and complex objects. OdeView provides facilities for examining the database schema (i.e., the object type or class hierarchy), examining class definitions, browsing objects, following chains of references starting from an object, synchronized browsing, displaying selected portions of objects (projection), and retrieving objects with specific characteristics (selection). OdeView does not need to know about the internals of Ode objects. Consequently, the internals of specific classes are not hardwired into OdeView and new classes can be added to the Ode database without requiring any changes to or recompilation of OdeView. Just as OdeView does not know about the object internals, class functions (methods) for displaying objects are written without knowing about the specifics of the windowing software used by OdeView or the graphical user interface provided by it. In this paper, we present OdeView, and discuss its design and implementation.
Evaluating the e-PASS: the psychometric properties and user experience of an online clinical diagnostic assessment program for mental disorders

Diagnostic assessment is critical for identifying and treating mental disorders. However, consumers face numerous barriers to effective diagnostic assessment in traditional clinical practice, including: stigma and poor mental health literacy; high costs and shortage of clinicians; and inadequate assessment resources and training for clinicians. With recent internet usage growth, web-based diagnostic assessment programs offer a means of overcoming these barriers while also providing benefits such as added accuracy and efficiency through automated item and scoring administration. However, they also raise issues which emphasise the need to ensure such programs are effective. At present, various web-based programs are publically available, although most present limited diagnostic functionality (e.g., narrow scope of disorders, limited feedback) and lack supporting research. This prompted development of the e-PASS, a freely available web-based clinical assessment program providing diagnostic and referral feedback for 21 disorders. To inform its appropriate use and address the dearth of empirical literature regarding similar applications, this thesis reports an evaluation of the e-PASS’s performance in real-world circumstances. Participants consisted of Australian adults (N = 616, M age = 38.1 years) recruited online and representing actual e-PASS users. In a psychometric study, participant subsets underwent: a structured clinical interview (n = 158); standardised clinical questionnaires (n = 173); and e-PASS re-testing (n = 39). With the clinical interview as the gold standard, the e-PASS’s diagnostic results displayed: strong agreement for some disorders (e.g. panic disorder) but not others (e.g. social phobia, OCD); high specificity, and varying sensitivity; similar or better accuracy compared to certain clinical questionnaires; and limited accuracy in primary diagnosis. Test-retest reliability was found to be high, while logistic regression indicated that reducing the diagnostic threshold and adding particular items could improve e-PASS validity. Two studies evaluated the e-PASS’s user experience, with participants (recruited from the psychometric study) completing an online survey (n = 88) or in-depth qualitative interviewing (n = 15). Results showed high e-PASS acceptability, including motivation to re-take the e-PASS for future needs. The e-PASS was widely praised for being more comprehensive and lessjudgmental than previous assessment experiences, promoting accessibility, anonymity, selfreflection, and disclosure. Group differences in experiences, as well as criticisms regarding length, restrictive response options, and lack of support in some cases highlighted the need to tailor online assessment to optimise outcomes. In conclusion, the e-PASS clearly demonstrates the potential to enhance traditional clinical assessment practice, and improve consumer mental health awareness and access to appropriate services.
An instrumental approach to deception in bargaining

Deception is often described as a form of unethical behavior, yet it is frequently used in bargaining. This raises the question why people use deception in bargaining. In this dissertation an instrumental approach to deception is presented to help answer this question. In an instrumental approach, the use of deception depends on both the goals bargainers pursue and the means they have to attain their goals. An instrumental approach presupposes that bargainers select the means they consider most instrumental to their current goal. This dissertation demonstrates that pursuing different goals or having different means, has an impact on the use of deception. Furthermore, it demonstrates that reactions to deceit also follow an instrumental pattern. Deceit was judged less harshly when it was employed by someone in a position with limited means. Finally, this dissertation shows that false expectations are an important reason why deception is considered unethical.
Nucleot(s)ide Analogues for Hepatitis B Virus-Related Hepatocellular Carcinoma after Curative Treatment: A Systematic Review and Meta-Analysis

Aim The benefit of nucleot(s)ide analogues (NA) for hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after curative treatment has been widely debated due to the relatively weak evidence. The objective of this systematic review was to evaluate the effect of NA on recurrence and survival after curative treatment of HBV-HCC. Methods A systematic electronic search was performed. All controlled trials comparing NA versus placebo or no treatment were considered for inclusion. Results were expressed as Hazard Ratio for recurrence and survival with 95% confidence intervals using RevMan 5.2. Results We included 13 trials with 6350 patients. There were significant improvements for recurrence-free survival (HR 0.66, 95% CI 0.54–0.80; p<0.0001) and overall survival (HR 0.56, 95% CI 0.43–0.73; p<0.0001) in the adjuvant NA group compared with the control group. Sensitivity analyses confirmed the robustness of the results. There were no serious adverse effects being reported. Lamivudine resistance was from 28.6% to 37.5% but could be rescued by other types of NA or combination therapy. Conclusion Our study suggested benefits of adjuvant NA therapy following curative treatment of HBV-HCC. Since the great proven efficacy of NA in improving clinical and viral parameters besides HCC, further studies should be focused on broadening the indications for NA therapy after curative treatment of HBV-HCC.
Hand posture recognition using Hidden Conditional Random Fields

Body-language understanding is essential to human robot interaction, and hand posture recognition is one of the most important components in a body-language recognition system. The existing hand posture recognition approaches based on robust local features such as SIFT can be invariant to background noise and in-plane rotation. However the ignorance of the relationships among local features is a fundamental issue. The part-based models argue that objects of the same category share the same part-structure which consists of parts and relationships among parts. In this paper, a discriminative part-based model, Hidden Conditional Random Fields (HCRFs), is used to recognize hand postures. Although the existing global locations of features have been used to consider large scale dependency among parts in the HCRFs framework, the results are not invariant to in-plane rotation. New features by the distance to the image center are proposed to encode the global relationship as well as to perform in-plane rotation-invariant recognition. The experimental results demonstrate that the proposed approach is in-plane rotation-invariant and outperforms the approach using AdaBoost with SIFT.
Magnitude of structural changes of the T-cell receptor binding regions determine the strength of T-cell antagonism: molecular dynamics simulations of HLA-DR4 (DRB1*0405) complexed with analogue peptide.

In our model system, we generated T cell clones specific for the HLA-DR4 (DRB1*0405)-index peptide (YWALEAAAD) complex. Based on response patterns of the T cell clones, analogue peptides containing single amino acid substitutions of the index peptide were classified into three types, agonists, antagonists or null peptides (non-agonistic and non-antagonistic peptides). Subtle structural changes induced by the antagonists in the T-cell receptor (TCR) binding regions have already been explained using the root mean square (r.m.s.) deviations from the DR4-index peptide complex in the molecular dynamics (MD) trajectory. In this work, we performed additional MD simulations at 300 K with explicit solvent molecules to reveal the structural character of the HLA-DR4 complexed with the analogue peptides. We examined the r.m.s. deviations of the TCR-binding sites and the exposed areas of the bound peptides. Remarkable differences of the r.m.s. deviations among the DR4-antagonist complexes, together with our previous data, suggest that the magnitude of structural changes of TCR-binding regions would determine the strength of TCR antagonism. The simulations also indicate that TCR could discriminate null peptides from other ligands mainly through the changes of exposed side chains of the bound peptide, rather than the conformational changes of TCR-binding surfaces on HLA molecule.
A Packet Size Control Algorithm for IEEE 802.16e

This paper proposes a novel algorithm which enables an ARQ protocol defined for IEEE 802.16e to vary the packet size depending on wireless channel behaviour. The algorithm adopts an analytical approach for dynamically evaluating the optimal packet size. The latter is derived from an expression of the ARQ protocol efficiency, obtained by exploiting a finite-state Markov error model which also considers Adaptive Modulation/Coding. The effectiveness of the designed algorithm in improving TCP performance has been evaluated.
Phonology and perception : A cognitive scientist ’ s perspective

As a researcher who has long been interested in the perception, use, and acquisition of language, the title of this volume, Phonology in Perception, already piques my interest. Closer examination reveals an exciting development: A diverse group of researchers grounded deeply in the discipline of linguistics are grappling with details of the actual human processing of language, something that would have been almost unthinkable just a few years ago. Every chapter speaks to issues of processing and learning about spoken language and refers to data from experimental psycholinguistics. These developments lend hope to the idea that the distinction between linguistic and psychological approaches to language will gradually fade away, replaced by an interdisciplinary investigation of language, encompassing the structure, use and acquisition of language and even language change. The remarks I make below are offered in the spirit of hastening this integration. The authors of the various chapters raise a number of issues and questions, either explicitly or implicitly, that lie at the heart of debates within psychological as well as linguistic circles.
Semi-supervised feature selection using co-occurrent frequent subgraphs

In the real world, there exist many graph applications that have only a small number of labeled graph data. Graph classification can achieve desired performances only if enough amounts of labeled data are available. Semi-supervised feature selection studies have been proposed to solve the short labeled data problem. However, the existing studies still do not meet satisfactory classification accuracy. In this paper, we propose a novel semi-supervised feature selection method, named co-occurrent graph feature selection (SCGFS) that selects a set of optimal co-occurrent frequent subgraph features for graph classification. Co-occurrent subgraphs can have higher discriminative powers than those of individual frequent subgraphs. Since co-occurrent patterns have an inefficiency problem, we propose a branch-and-bound search that reduces the feature search space with effective pruning techniques. Through comprehensive experiments, we show that the proposed framework can efficiently select more discriminative subgraph features compared with existing semi-supervised feature selection algorithm.
Training of neural network ensemble through progressive interaction

This paper presents an interactive training method for neural network ensembles (NNEs). For an NNE, proposed method trains component neural networks (NNs) one after another sequentially and interactions among the NNs are maintained indirectly via an intermediate space, called information center (IC). IC manages outputs of all previously trained NNs. Update rule, to train an NN in conjunction with IC, is developed from negative correlation learning (NCL) and defined the proposed method as progressive NCL (pNCL). The introduction of such an information center in ensemble methods reduces the training time interaction among component NNs. The effectiveness of the proposed method is evaluated on several benchmark classification problems. The experimental results show that the proposed approach can improve the performance of NNEs. pNCL is incorporated with two popular NNE methods, bagging and boosting. It is also found that the performance of bagging and boosting algorithms can be further improved by incorporating pNCL with their training processes.
An optimized derivative projection warping approach for moving platform video stabilization

This paper presents an optimized and efficient video stabilization technique based on projection curve warping. In most of the recorded videos, the relative displacement between two consecutive frames goes from 3-4 pixel for hand-held and 25-30 for moving platform applications. Based on this experimental data, the use of Sakoe-Chiba band with fixed window size has been proposed for constraining distance matrix estimation, in the dynamic time warping algorithm. In the existing projection based stabilization techniques, intensity values are matched for motion estimation. Any change in the local intensity values either induced due to intensity variation, moving objects or scene variation, causes error in the estimated motion. To overcome this problem, a higher level feature i.e. shape of the projection curve has been incorporated by matching the local derivative of curve instead of the intensity values itself. Robustness and time efficiency of the proposed technique is measured in terms of interframe transformation fidelity and processing time respectively.
Dancing with Wolves: Towards Practical Event-driven VMM Monitoring

This paper presents a novel framework that enables practical event-driven monitoring for untrusted virtual machine monitors (VMMs) in cloud computing. Unlike previous approaches for VMM monitoring, our framework neither relies on a higher privilege level nor requires any special hardware support. Instead, we place the trusted monitor at the same privilege level and in the same address space with the untrusted VMM to achieve superior efficiency, while proposing a unique mutual-protection mechanism to ensure the integrity of the monitor. Our security analysis demonstrates that our framework can provide high-assurance for event-driven VMM monitoring, even if the highest-privilege VMM is fully compromised. The experimental results show that our framework only incurs trivial performance overhead for enforcing event-driven monitoring policies, exhibiting tremendous performance improvement on previous approaches.
Movement of the shoulder complex: the development of a measurement technique based on proposed ISB standards

This paper introduces a protocol for the measurement of shoulder movement that uses a motion analysis based technique and the proposed standards of the International Society of Biomechanics. The protocol demonstrates effective dynamic tracking of shoulder movements in 3D, including the movement of the thorax relative to the global coordinate system, the humerus relative to the thorax, the scapula relative to the thorax, the sternoclavicular joint, the acromioclavicular joint and the glenohumeral joint. This measurement protocol must be further tested for accuracy and repeatability using motion and imaging data from existing methods developed prior to the ISB recommendations. It is proposed to apply the validated model to assess pathological shoulder movement and function with the aim of developing a valuable clinical diagnostic tool to aid surgeons in identifying optimum treatment strategies.
Efficacy of mood stabilisers in the treatment of impulsive or repetitive aggression: systematic review and meta-analysis

Background Individuals with repetitive or impulsive aggression in the absence of other disorders may be diagnosed with intermittent explosive disorder according to DSM–IV, but no such diagnostic category exists in ICD–10. Mood stabilisers are often used off-license for the treatment of aggression associated with a variety of psychiatric conditions, but their efficacy in these and in idiopathic aggression is not known. Aims To summarise and evaluate the evidence for the efficacy of mood stabilisers (anticonvulsants/lithium) in the treatment of impulsive or repetitive aggression in adults. Method A meta-analysis of randomised controlled trials that compared a mood stabiliser with placebo in adults without intellectual disability, organic brain disorder or psychotic illness, identified as exhibiting repetitive or impulsive aggression. Results Ten eligible trials (489 participants) were identified A pooled analysis showed an overall significant reduction in the frequency/severity of aggressive behaviour (standardised mean difference (SMD) =–1.02, 95% CI −1.54 to −0.50), although heterogeneity was high (I 2 = 84.7%). When analysed by drug type, significant effects were found in the pooled analysis of three phenytoin trials (SMD =–1.34, 95% CI −2.16 to −0.52), one lithium trial (SMD =–0.81, 95% CI −1.35 to −0.28), and two oxcarbazepine/carbamazepine trials (SMD =–1.20, 95% CI −1.83 to −0.56). However, when the results of only those studies that had a low risk of bias were pooled (347 participants), there was no significant reduction in aggression (SMD =–0.28, 95% CI −0.73 to 0.17, I 2 = 71.4%). Conclusions There is evidence that mood stabilisers as a group are significantly better than placebo in reducing aggressive behaviour, but not all mood stabilisers appear to share this effect. There is evidence of efficacy for carbamazepine/oxcarbazepine, phenytoin and lithium. Many studies, however, were at risk of bias and so further randomised controlled trials are recommended.
Generalized stereo for hybrid omnidirectional and perspective imaging

In this paper we present a general framework for the hybrid omnidirectional and perspective imaging system. Based on the hybrid imaging geometry, a generalized stereo approach is developed via the construction of virtual cameras. It is then used to rectify the hybrid image pair using the perspective projection model. The proposed method not only simplifies the computation of epipolar geometry for the hybrid imaging system, but also facilitates the stereo matching between the heterogeneous image formation. Experimental results for both the synthetic and real scene images have demonstrated the feasibility of our approach.