Miracle-dz/UniFAR-FTUs huggingface dataset:
Faithful Completion of Images of Scenic Landmarks Using Internet Images

Previous works on image completion typically aim to produce visually plausible results rather than factually correct ones. In this paper, we propose an approach to faithfully complete the missing regions of an image. We assume that the input image is taken at a well-known landmark, so similar images taken at the same location can be easily found on the Internet. We first download thousands of images from the Internet using a text label provided by the user. Next, we apply two-step filtering to reduce them to a small set of candidate images for use as source images for completion. For each candidate image, a co-matching algorithm is used to find correspondences of both points and lines between the candidate image and the input image. These are used to find an optimal warp relating the two images. A completion result is obtained by blending the warped candidate image into the missing region of the input image. The completion results are ranked according to combination score, which considers both warping and blending energy, and the highest ranked ones are shown to the user. Experiments and results demonstrate that our method can faithfully complete images.
A Two Way Validation Framework for Cloud Storage Security

High Performance Computing (HPC) has become one of the predominant techniques for processing the large scale applications. Cloud environment has been chosen to provide the required services and to process these high demand applications. Management of such applications challenges us on three major things i.e. network feasibility, computational feasibility and data security. Several research endeavours are focused on network load and computing cloud date and provided better outcomes. Still those approaches are not able to provide standard mechanisms in view of data security. On the other side, research towards enabling the auditing features on the cloud based data by various researchers has been addressed but their performance is poor. However, the complexity of the audit process proven to be the bottleneck in improving performance of the application as it consumes the computational resources of the same application. Henceforth, this work proposes a novel framework for cloud data auditing at multiple levels to audit the access requests and upon validating the conditions of one level, the connection request will be moved to the further complex levels in order to reduce the computational loads. The proposed framework determines a substantial reduction in the computational load on the cloud server, thus improves the application performance leveraging the infrastructure use.
Scalable Parallel Algorithms for Shared Nearest Neighbor Clustering

Clustering is a popular data mining technique which discovers structure in unlabeled data by grouping objects together on the basis of a similarity criterion. Traditional similarity measures lose their meaning as the number of dimensions increases and as a consequence, distance or density based clustering algorithms become less meaningful. Shared Nearest Neighbor (SNN) is a solution to clustering high-dimensional data with the ability to find clusters of varying density. SNN assigns objects to a cluster, which share a large number of their nearest neighbors. However, SNN is compute and memory intensive for data of large size and/or dimensionality. Nearest neighbor queries are responsible for a major proportion of computations in SNN, resulting in lower efficiency for higher value of number of nearest neighbors (k). The main motivation of this work is to improve the efficiency of SNN and to parallelize it so that it can be used for clustering large high-dimensional datasets and for large values of k. Existing SNN algorithms become inefficient in these situations. In this paper, we present a new sequential SNN algorithm, R-SNN, which uses R-tree for executing neighborhood queries efficiently and exploiting spatial locality to minimize memory usage. R-SNN is benchmarked against the best available implementation of SNN and is found up to 77 times faster when tested on various real datasets. R-SNN is parallelized for distributed memory, shared memory, and hybrid systems. Significant speedup and scalability achieved can be attributed to parallelization and good load balancing strategies and also to exploitation of spatial locality. Experimental results demonstrate the same for datasets of varying dimensionality and size. The maximum speedup achieved for shared, distributed, and hybrid models are 427.19 using 48 threads, 394.24 using 32 processes, and 1380.69 on 32 nodes (with each node spawning 4 threads), respectively. Super-linear speedup for some datasets is attributed to optimized neighborhood queries. All the proposed algorithms produce identical clustering results as that of the classical SNN.
Appreciative Inquiry for stress management

Purpose The purpose of this paper is to demonstrate the innovative application of an Appreciative Inquiry (AI) approach for the design and implementation of organizational stress management interventions, alongside a case study of the successful design and implementation of the approach. By utilizing the AI methodology to develop a “local stress theory” for the participating organization, the authors propose a model which can be utilized in other similar organizations. Design/methodology/approach Stage 1: 35 participants completed up to ten daily logs by answering four positively framed questions regarding their working day. Stage 2: semi-structured interviews (n=13). The interview schedule was designed to further elaborate log findings, and begin looking into feasible organizational changes for improvement of stress. Stage 3: two focus groups (Stage 3, total 13 employees) verified interventions from logs and interviews and discuss how these can be implemented. Findings The log phase identified two key themes for improvement: managerial/organizational support and communication. From these, interviews and focus groups led to workable proposals for simple but likely effective changes. The authors reported findings to management, emphasizing organizational change implementation, and these were subsequently implemented. Research limitations/implications The study demonstrated the effectiveness of AI to identify and implement relatively simple but meaningful changes. The AI cycle was completed but allocating lengthy follow-up time for evaluation of outcomes was not possible, although initial responses were favorable. There are also issues of generalizability of the findings. Originality/value This is the among first studies to utilize an AI approach for the design of stress management interventions.
Message-Efficient CDS construction in MANETs

The connected dominating set (CDS) has been extensively used for routing and broadcast in mobile ad hoc networks (MANETs). Due to the nature of MANETs, it is preferred that the CDS protocol not only creates a CDS with small size, but also incurs less communication and computational overhead, adapts to the nodal mobility, and generates CDS as quickly as possible. While the CDS protocols developed in the past create small size of CDS, they tend to incur too much communication overhead. In this paper, we propose the Message-Efficient Dominator Tree Connection algorithm (ME-DTC), which significantly reduces the number of control messages during the tree connection phase in Tree-based CDS protocols. By incorporating this algorithm with the Multi-Initiator CDS protocol, we have a CDS protocol which meets all the desirable features in MANETs. The simulation as well as analytical results validate that our proposed algorithm achieves its design goals.
Advances in the Growth and Characterization of Relaxor-PT-Based Ferroelectric Single Crystals

Compared to Pb(Zr1−xTix)O3 (PZT) polycrystalline ceramics, relaxor-PT single crystals offer significantly improved performance with extremely high electromechanical coupling and piezoelectric coefficients, making them promising materials for piezoelectric transducers, sensors and actuators. The recent advances in crystal growth and characterization of relaxor-PT-based ferroelectric single crystals are reviewed in this paper with emphases on the following topics: (1) the large crystal growth of binary and ternary relaxor-PT-based ferroelectric crystals for commercialization; (2) the composition segregation in the crystals grown from such a solid-solution system and possible solutions to reduce it; (3) the crystal growth from new binary and ternary compositions to expand the operating temperature and electric field; (4) the crystallographic orientation dependence and anisotropic behaviors of relaxor-PT-based ferroelectriccrystals; and (5) the characterization of the dielectric, elastic and piezoelectric properties of the relaxor-PT-based ferroelectriccrystals under small and large electric fields.
Insect-Induced Daidzein, Formononetin and Their Conjugates in Soybean Leaves

In response to attack by bacterial pathogens, soybean (Gylcine max) leaves accumulate isoflavone aglucones, isoflavone glucosides, and glyceollins. In contrast to pathogens, the dynamics of related insect-inducible metabolites in soybean leaves remain poorly understood. In this study, we analyzed the biochemical responses of soybean leaves to Spodoptera litura (Lepidoptera: Noctuidae) herbivory and also S. litura gut contents, which contain oral secretion elicitors. Following S. litura herbivory, soybean leaves displayed an induced accumulation of the flavone and isoflavone aglycones 4’,7-dihyroxyflavone, daidzein, and formononetin, and also the isoflavone glucoside daidzin. Interestingly, foliar application of S. litura oral secretions also elicited the accumulation of isoflavone aglycones (daidzein and formononetin), isoflavone 7-O-glucosides (daidzin, ononin), and isoflavone 7-O-(6’-O-malonyl-β-glucosides) (malonyldaidzin, malonylononin). Consistent with the up-regulation of the isoflavonoid biosynthetic pathway, folair phenylalanine levels also increased following oral secretion treatment. To establish that these metabolitic changes were the result of de novo biosynthesis, we demonstrated that labeled (13C9) phenylalanine was incorporated into the isoflavone aglucones. These results are consistent with the presence of soybean defense elicitors in S. litura oral secretions. We demonstrate that isoflavone aglycones and isoflavone conjugates are induced in soybean leaves, not only by pathogens as previously demonstrated, but also by foliar insect herbivory.
The neural basis for response latency in a sensory-motor behavior

We seek a neural circuit explanation for sensory-motor reaction times. We have found evidence that two of three possible mechanisms could contribute to reaction times in smooth pursuit eye movements. In the smooth eye movement region of the frontal eye fields (FEFSEM), an area that causally affects the initiation of smooth pursuit eye movement, neural and behavioral latencies have significant trial-by-trial correlations that can account for 40% to 100% of the variation in behavioral latency. The amplitude of preparatory activity, which represents the motor system’s expectations for target motion, shows negative trial-by-trial correlations with behavioral latency and could contribute to the neural computation of reaction time. In contrast, the traditional “ramp-to-threshold” model is contradicted by the responses of many, but not all FEFSEM neurons. As evidence of neural processing that determines reaction time, the local field potential in FEFSEM includes a brief wave in the 5-15 Hz frequency range that precedes pursuit initiation and whose phase is correlated with the latency of pursuit in individual trials. We suggest that the latency of the incoming visual motion signals combines with the state of preparatory activity to determine the latency of the transient response that drives eye movement.
E-Government Diffusion: Evidence from the Last Decade

The use of technology to offer goods and services by governments to citizens has been a growing phenomenon in the last decade. Many projects have been recorded across nations in an effort to encourage the adoption and diffusion of e-government. However, these studies have been rather isolated project reports than a comprehensive global picture. This study examines the e-government diffusion across 192 countries in the last decade. The panel data obtained is analyzed to evaluate the current state of e-government diffusion. Evidence in the last decade suggests that the difference in e-government diffusion is wider from country to country, than from year to year, within the same country and that country-specific effects account for these differences. The authors discuss the implications of these findings.
Versican G3 Domain Modulates Breast Cancer Cell Apoptosis: A Mechanism for Breast Cancer Cell Response to Chemotherapy and EGFR Therapy

Overexpression of EGFR and versican has been reported in association with breast cancers. Considered oncogenic, these molecules may be attractive therapeutic targets. Possessing anti-apoptotic and drug resistant properties, overexpression of these molecules is accompanied by selective sensitization to the process of apoptosis. In this study, we exogenously expressed a versican G3 construct in breast cancer cell lines and analyzed the effects of G3 on cell viability in fetal bovine serum free conditioned media and evaluated the effects of apoptotic agent C2-ceramide, and chemotherapeutic agents including Docetaxel, Doxorubicin, and Epirubicin. Versican G3 domain enhanced tumor cell resistance to apoptosis when cultured in serum free medium, Doxorubicin, or Epirubicin by up-regulating pERK and GSK-3β (S9P). However, it could be prevented by selective EGFR inhibitor AG 1478 and selective MEK inhibitor PD 98059. Both AG 1478 and PD 98059 enhanced expression of pSAPK/JNK, while selective JNK inhibitor SP 600125 enhanced expression of GSK-3β (S9P). Versican G3 promoted cell apoptosis induced by C2-ceramide or Docetaxel by enhancing expression of pSAPK/JNK and decreasing expression of GSK-3β (S9P), an observation blocked by AG 1478 or SP 6000125. Inhibition of endogenous versican expression by siRNA or reduction of versican G3's expression by linking G3 with 3′UTR prevented G3 modulated cell apoptosis. The dual roles of G3 in modulating breast cancer cell resistance to chemotherapeutic agents may in part explain a potential mechanism for breast cancer cell resistance to chemotherapy and EGFR therapy. The apoptotic effects of chemotherapeutics depend upon the activation and balance of down stream signals in the EGFR pathway. GSK-3β (S9P) appears to function as a key checkpoint in this balance of apoptosis and anti-apoptosis. Investigation and potential consideration of targeting GSK-3β (S9P) merits further study.
Comparing Kaplan–Meier curves with delayed treatment effects: applications in immunotherapy trials

We consider a comparison of Kaplan–Meier curves from clinical trials in which there may be a delayed treatment effect. Any such delay takes us outside the umbrella of a proportional hazards structure and therefore outside the setting in which the log‐rank test would be optimal. The approach of Chauvel and O’Quigley based on Brownian motion approximations enables the construction of powerful tests in situations of non‐proportionality and, in particular, a powerful test in the situation of delayed effect. The power of this test is seen to be very close to that of the most powerful test, which, however, is unavailable in practice. We show that the test is unbiased and consistent under general conditions. Under the null, we obtain identical large sample behaviour to the log‐rank test so the type 1 error is correctly controlled. Under proportional hazards departures from the null we obtain results that indicate a manageable loss in power compared with the log‐rank test. The usual sample size calculations can still provide a useful guide. Support for the theoretical findings are provided by simulations as well as illustrations from three immunotherapy clinical trials.
Diversity-Enhanced Conversational Collaborative Recommendations

In conversational collaborative recommender systems, user feedback inﬂuences the recommendations. We summarise the seminal work in this ﬁeld [5] and make precise a variant in which the likes and dislikes of other users in the system are distinguished when matching against the active user’s short-term positive and negative proﬁles. But the major innovation that we report is our mechanism for enhancing the diversity of the recommendations made by collaborative recommenders. Signiﬁcantly, we increase diversity using collaborative data only. In our experiments with diversity-enhanced recommendations, users ﬁnd target items in many fewer recommendation cycles.
Pieri's formula for flag manifolds and Schubert polynomials

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Performance Pathologies in Hardware Transactional Memory

Transactional memory is a promising approach to ease parallel programming. Hardware transactional memory system designs reflect choices along three key design dimensions: conflict detection, version management, and conflict resolution. The authors identify a set of performance pathologies that could degrade performance in proposed HTM designs. Improving conflict resolution could eliminate these pathologies so designers can build robust HTM systems.
R2D2V: RNC based Regional Data Distribution on VANETs

In this paper, we discuss a method for continuously providing location-dependent information generated by vehicles to other vehicles being driven in the surrounding areas of a location where the information was generated during the information effctive period using Vehicular Ad hoc NETworks (VANETs). When many vehicles simultaneously disseminate data items in an area where the vehicle density is high by using opportunistic way on VANETs, many redundant packets are sent, and this leads to many packet collisions, and wastes wireless communication resources. To tackle the problem, we designed a scheme called R2D2V (RNC-based Regional Data Distribution on VANETs). R2D2V consists of (1) using random network coding (RNC), (2) adaptive changing Hello sending intervals, and (3) adaptive changing the probability of replying to Hello messages according to the vehicular density. Simulation results show that this scheme achieves a high data delivery ratio with low data delivery traffic and low delay when the vehicle density is high.
Teaching mindfulness online

This article outlines the design and delivery of sessions used and the results obtained from a 2016 pilot study investigating whether the use of mindfulness-based techniques could enhance the wellbeing and academic performance of university students. The study, a collaboration between counselling and chaplaincy at Charles Sturt University (CSU) had three aims: (1) to determine benchmark data of wellbeing, focused attention and academic motivation across the CSU student population; (2) to determine whether mindfulness training affected the emotional wellbeing, focused attention and academic performance of higher education students; and (3) because around 60% of CSU students study online, to examine the relative contribution of two different online modes for delivering mindfulness training, one in real time and one self-guided. Based on research that supports the assumption that ‘mindful learning’ benefits students by enhancing their cognitive and socio-emotional capabilities and improving their general wellbeing and academic performance, this study used three validated psychometric scales to measure the attention and awareness, wellbeing and academic motivation of the participants before and after a four week mindfulness training course. The results demonstrated three main findings: mindfulness training may contribute in the long-term to creating a mindset conducive to learning; mindfulness training can be successfully administered online, both in real time and self-guided; and mindfulness training may contribute to shifting patterns of motivation for learning from extrinsic to intrinsic motivation.
Youth negotiation of citizenship identities in Pakistan: Implications for global citizenship education in conflict-contexts

ABSTRACT This study explores young students’ negotiation of their citizenship identities at the intersection of their class, gender, religious and ethnic identifications in the conflict-affected setting of Pakistan. While much of the global literature on global citizenship education (GCE) primarily takes into account the perspectives of middle-class or elite students located in richer economies, the current study is centred on a socio-demographically diverse group of young people in a low-income setting. With a specific focus on their negotiation of issues around diversity and justice, students’ narratives generated important recommendations for a transformative and historically nuanced postcolonial/decolonial approach to global citizenship engagement that should be considered more broadly. The study illuminates the ways the global/local historical, cultural, political and economic factors influence individual relationship with GCE and offers useful pedagogical and policy implications for GCE ‘from below’.
Research report-Automated runnable to task mapping

We propose in this paper, a method to automatically map runnables (blocks of code with dedicated functionality) with real-time constraints to tasks (or threads). We aim at reducing the number of tasks runnables are mapped to, while preserving the schedulability of the initial system. We consider independent tasks running on a single processor. Our approach has been applied with fixed-task or fixed-job priorities assigned in a Deadline Monotonic (DM) or a Earliest Deadline First (EDF) manner.
Spatially varying bi-directional reflectance distribution functions

This paper describes briefly what a bi-directional reflectance distribution function is. The paper explains Lafortune BRDF representation, which is a compact and expressive model suitable for real-time rendering. Two methods for rendering Lafortune BRDF with spatial variation over surface are portrayed. The first one is for rendering with point lights and the second one with environment map illumination. The implementation of the first method with NVIDIA GeForce 4 is described. The paper also discusses possibilities that more modern hardware gives.
Cognitive learning enabled real time object search robot

Object Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Most works are focused on a specific application, such as tracking human, car, or pre-learned objects. All these require database and considerable amount of training time to detect the current object and to track it. In this paper we propose a method to track objects where a pre-stored database is not a requirement. The proposed method uses a combination of Scale Invariant Feature Transform (SIFT) based feature extraction, Kalman filter and Cognitive learning. The algorithm has the ability to make its own database of the objects in the due course of time by interacting with the user through text based communication. This algorithm is deployed on a search robot which does the operation of searching an object in real time upon a command from the user. The search operation of robot is made more flexible using Bluetooth wireless communication protocol.