Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoothing Via Iterative Averaging (SIA) A Basic Technique for Line Smoothing

—Line Smoothing is the process of curving lines to make them look smoother, better to say it is the representation of a polyline so that fewer points represent the caricature of the line. It also usually reduces the noise in a signal. This can apply to a vector, spline, or a list of points corresponding to a line or signal. Many algorithms are available for automated line smoothing that is commonly seen as a comparatively simple operation; however, instructions for using these algorithms are often complex. In this paper, we present a new method as a basic technique that can efficiently smooth a list of points. We focus on preserving characteristics of the line while avoiding any distortions. Our goal is to demonstrate a flexible method to preserve features of the input based on its characteristics with fewer constants. Since this technique can apply to both vectors and lists of points, it is also useful in map generalization. Selected test examples are illustrated and discussed, followed by an assessment of the models. Finally, results of the proposed method are examined, showing more stable preservation and better noise reduction compared to the available methods reported in the literature.
Joint Feature Learning for Face Recognition

This paper presents a new joint feature learning (JFL) approach to automatically learn feature representation from raw pixels for face recognition. Unlike many existing face recognition systems, where conventional feature descriptors, such as local binary patterns and Gabor features, are used for face representation, we propose an unsupervised feature learning method to learn hierarchical feature representation. Since different face regions have different physical characteristics, we propose to use different feature dictionaries to represent them, and to learn multiple yet related feature projection matrices for these regions simultaneously. Hence position-specific discriminative information can be exploited for face representation. Having learned these feature projections for different face regions, we perform spatial pooling for face patches within each region to enhance the representative power of the learned features. Moreover, we stack our JFL model into a deep architecture to exploit hierarchical information for feature representation and further improve the recognition performance. Experimental results on five widely used face data sets show the effectiveness of our proposed approach.
Self-healing Deterministic Expanders

We present fully-distributed algorithms that construct and maintain deterministic expander networks (i.e., the expansion properties are deterministically guaranteed) in the presence of an adaptive adversary. To the best of our knowledge, these algorithms are the first distributed implementations of deterministic expanders that work even under an all-powerful adaptive adversary (that has unlimited computational power and knowledge of the entire network state, can decide which nodes join and leave and at what time, and knows the past random choices made by the algorithm). Previous distributed expander constructions typically provide only probabilistic guarantees and these rapidly degrade over a series of network changes, so here we provide a much needed solution. Our algorithms are in a self-healing model where at each step, the omniscient adversary either inserts or deletes a node and the algorithm responds by locally adding or dropping edges (connections) in the network. Our algorithms provide fast healing with high probability in O(log n) rounds on average, where n is the number of nodes currently in the network, while deterministically maintaining a constant degree expander. Moreover, our algorithms guarantee that the number of added/dropped edges per round is constant on average. Our communication model follows the CONGEST model, allowing messages of size only O(log n). This ensures that our algorithms are highly scalable. We also present a lower bound of Θ(log n) rounds on average, for any distributed expander self-healing algorithm. This shows that our algorithms are asymptotically the best possible (up to constant factors) in this model. Our work also yields an improved version of the self-healing algorithm Xheal [PODC 2011], which previously relied on expander constructions with only probabilistic guarantees.
Regression-based multi-marker analysis for genome-wide association studies using haplotype similarity

Although haplotype analyses have been prevalent in genetic association studies, the main statistical analyses in genome-wide association (GWA) studies still mostly focus on singleSNP analyses. Several practical issues hinder practitioners’ enthusiasm to perform haplotypebased analysis under a GWA setting, including the need for large degrees of freedom and the treatment of missing phase information. To avoid these pitfalls, we propose a similarity-based regression model. It captures genetic variants via haplotype similarity to reduce the degrees of freedom, and uses phase-independent similarity measures to bypass the needs to impute phase information. We construct the score test to detect association between trait similarity and genetic similarity, and identify the limiting distribution of the score statistic. We show that the gene-trait similarity regression is closely connected with the random effects haplotype analysis although commonly they are considered as separate modeling tools in haplotype analysis. The proposed method is computationally efficient, allows for covariates and is applicable to both quantitative and binary traits. It serves as an effective tool for multi-marker analysis in genomewide association studies.
A PVSS Scheme Based on Boolean Operations with Improved Contrast

Visual Secret Sharing (VSS) is a method to encode a secret image into multiple shares. Coding one pixel of secret image into m sub-pixels in shares during encoding process causes pixel expansion problem. Probabilistic Visual Secret Sharing Scheme (PVSS) are proposed in recent years to overcome this problem. In traditional VSS and PVSS methods, human visual system is sufficient for recovering the secret image. In 2007, Wang proposed a (2, n) probabilistic method that uses Boolean operations for encoding and decoding of secret image. In this study, we proposed a method for generating random B matrices that are used by Wang's method. Accuracy of the reconstructed white pixels is improved. Maximum PSNR value of the reconstructed image with the proposed method will be higher than that of Wang's PSNR value. One of the most important criterion for evaluation of VSS schemes is accuracy of reconstructed pixels and proposed method yields improved accuracy compared to Wang's schemes.
The Vicious Cycle: Interrelationship among trauma, substance misuse and offending from the perspective of Scottish incarcerated women – an Interpretative Phenomenological Analysis.

Background: Existing literature strongly supports the existence of significant relationships among trauma, substance misuse and offending in incarcerated women. However, these experiences are rarely explored simultaneously, the understanding of the nature of this interrelationship is limited, and this area of research is dominated by quantitative, non-UK studies. Aims: This study explored the chronological order and the mechanisms underpinning the interrelationship among trauma, substance misuse and offending from the perspective of Scottish incarcerated women. Method: An Interpretative Phenomenological Approach (IPA) was adopted and semi-structured interviews supported by Life History Calendars (LHCs) were conducted with eight Scottish incarcerated women. Results: The results from the LHCs revealed that trauma, substance misuse and offending can occur in any order and these experiences can continue following one another in all directions. Four main themes emerged as a result of the IPA analysis: “living in a hostile environment”, “making sense of self and the world”, “dysregulation of the emotion management system” and “ma life was spiralling out of control”. The mechanisms identified to underpin the interrelationship among trauma, substance misuse and offending included avoidant coping, losing control over behaviour, venting negative emotions, normalisation, financially supporting addiction and many others. Conclusions: Findings revealed a cyclical, multidirectional and multifaceted interrelationship among trauma, substance misuse and offending, creating a vicious cycle underpinned by a number of mechanisms. The detailed insight into the nature of this interrelationship carries significant implications for education, policy and clinical practice.
Using Principal Component Analysis and Hidden Markov Model for Hand Recognition Systems

There are many approaches and algorithms that can be used to recognize and synthesize the hands gesture. Each approach has its own advantages and characteristics. This paper describes the usage of Hidden Markov Models (HMM) and Principal Component Analysis (PCA) in recognizing hands gesture by two different researches. The limitations of each techniques and comparisons between each other will be detailed below.
Query log driven web search results clustering

Different important studies in Web search results clustering have recently shown increasing performances motivated by the use of external resources. Following this trend, we present a new algorithm called Dual C-Means, which provides a theoretical background for clustering in different representation spaces. Its originality relies on the fact that external resources can drive the clustering process as well as the labeling task in a single step. To validate our hypotheses, a series of experiments are conducted over different standard datasets and in particular over a new dataset built from the TREC Web Track 2012 to take into account query logs information. The comprehensive empirical evaluation of the proposed approach demonstrates its significant advantages over traditional clustering and labeling techniques.
The impact of factors within the work environment on perceptions of training transfer : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Psychology at Massey University

The current study was designed to investigate empirically the direct effects of various components of the work environment on perceptions of training transfer. The influence of social support from four organisational constituents (peer support, subordinate support, supervisor support, and top management support), organisational commitment and task constraints in the work environment and training transfer were evaluated. The sustained use of trained skills was also considered in the current study. Differences in perceptions of training transfer two weeks and twelve weeks after training were measured, as were the impact of each of the independent variables at each timepoint. The data was examined using correlation analyses and regression modeling. Results indicated that there were no significant differences in perceived training transfer between the time periods, but different variables were identified as important at each timepoint. Two weeks after training , top management support and organisational commitment explained 32.4 percent of the variance. Twelve weeks after training , organisational commitment was the only significant variable, explaining 31 . 1 percent of the variance. Results indicated some support for a positive relationship between social support and transfer. Two weeks after training , there was a relationship between transfer and supervisor support, top management support and peer support. Twelve weeks after training, the only relationship was between transfer and subordinate support. There was no relationship between task constraints and perceived training transfer. The implications of these results are discussed in relation to previous research as well as practical implications for organisations and training practitioners.
Determinants for the successful establishment of exotic ants in New Zealand

Biological invasions can dramatically alter ecosystems. An ability to predict the establishment success for exotic species is important for biosecurity and conservation purposes. I examine the exotic New Zealand ant fauna for characteristics that predict or determine an exotic species’ ability to establish. Quarantine records show interceptions of 66 ant species: 17 of which have established, 43 have failed to establish, whereas nests of another six are periodically observed but have failed to establish permanently (called ‘ephemeral’ establishment). Mean temperature at the highest latitude and interception variables were the only factors significantly different between established, failed or ephemeral groups. Aspects of life history, such as competitive behaviour and morphology, were not different between groups. However, in a stepwise discriminant analysis, small size was a key factor influencing establishment success. Interception rate and climate were also secondarily important. The resulting classification table predicted establishment success with 71% accuracy. Because not all exotic species are represented in quarantine records, a further discriminant model is described without interception data. Though with less accuracy (65%) than the full model, it still correctly predicted the success or failure of four species not used in the previous analysis. Techniques for improving the prediction accuracy are discussed. Predicting which species will establish in a new area appears an achievable goal, which will be a valuable tool for conservation biology.
Connexin 43 and Its Hemichannels Mediate Hypoxia–Ischemia-Induced Cell Death in Neonatal Rats

Wistar rat pups had the left common carotid artery cut, and they were exposed to 8% oxygen with free access to food and water until they were killed at 1, 12, 24, and 48 hours after the hypoxia–ischemia (HI) insult. Connexin 43 (Cx43), hemichannel (HC1), and caspase 3 (Casp3) in cerebral HI tissues were examined by immunohistochemistry and Western blot analyses. Astrocytes cell line, astrocytes transduced with a retroviral empty vector (Psup astrocyte), or a Cx43-specific small hairpin RNA (shRNA) construct (shRNA astrocytes) was treated with oxygen–glucose deprivation (OGD) insult. The viability of astrocytes was assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The results showed the expression of Cx43, HC1, and Casp3 in rats’ brain, and astrocytes and Psup astrocytes increased significantly after 24 hours of HI/OGD insult. Cell viability decreased after 24 hours of the insult. The results suggest that Cx43 and hemichannel are likely to mediate the astrocytic death after HI insult.
Road detection system based on RGB histogram filterization and boundary classifier

The purpose of this paper is to describe a new approach in road detection. This research uses two detection processes approaches: RGB histogram Filterization and Boundary Classifer, which is different from previous works on road detection. RGB Histogram Filterization processes the reading from the camera in greyscale form and afterward processes them by color segmentation. The last step for this process is determining area between the slopes, which is considered to be the road area. Boundary classification process then employs the RGB indexing on slope ranges, and mapping them into real pictures of roads and its environments. The next process is specifically looking for line boundaries by using Hough-Transform and Canny Edge Detection, and transforms them into binary numbers of `0' and `1'. `1' represents road boundaries while `0' represents surrounding area. The coordinate of `1', then mapped by cubic spline to produce connecting line between point `1' coordinates, which in the end produce sharp images on boundaries between road and non-road. This model has proven to be able to detect road conditions and distinguish roads from non-road in a precise way. A test is already conducted for the system by using real-time roads in Bandung, Indonesia. The results are really promising for the road condition on both straight and curved road area.
Proteomics, metabolomics, and ionomics perspectives of salinity tolerance in halophytes

Halophytes are plants which naturally survive in saline environment. They account for ∼1% of the total flora of the world. They include both dicots and monocots and are distributed mainly in arid, semi-arid inlands and saline wet lands along the tropical and sub-tropical coasts. Salinity tolerance in halophytes depends on a set of ecological and physiological characteristics that allow them to grow and flourish in high saline conditions. The ability of halophytes to tolerate high salt is determined by the effective coordination between various physiological processes, metabolic pathways and protein or gene networks responsible for delivering salinity tolerance. The salinity responsive proteins belong to diverse functional classes such as photosynthesis, redox homeostasis; stress/defense, carbohydrate and energy metabolism, protein metabolism, signal transduction and membrane transport. The important metabolites which are involved in salt tolerance of halophytes are proline and proline analog (4-hydroxy-N-methyl proline), glycine betaine, pinitol, myo-inositol, mannitol, sorbitol, O-methylmucoinositol, and polyamines. In halophytes, the synthesis of specific proteins and osmotically active metabolites control ion and water flux and support scavenging of oxygen radicals under salt stress condition. The present review summarizes the salt tolerance mechanisms of halophytes by elucidating the recent studies that have focused on proteomic, metabolomic, and ionomic aspects of various halophytes in response to salinity. By integrating the information from halophytes and its comparison with glycophytes could give an overview of salt tolerance mechanisms in halophytes, thus laying down the pavement for development of salt tolerant crop plants through genetic modification and effective breeding strategies.
HiPPIS: an online P2P system for efficient lookups on d-dimensional hierarchies

In this paper we describe HiPPIS, a system that enables efficient storage and on-line querying of multidimensional data organized into concept hierarchies and dispersed over a network. Our scheme utilizes an adaptive algorithm that automatically adjusts the level of indexing according to the granularity of the incoming queries, without assuming any prior knowledge of the workload. Efficient roll-up and drill-down operations take place in order to maximize the performance by minimizing query flooding. Extensive experimental evaluations show that, on top of the advantages that a distributed storage offers, our method answers the large majority of incoming queries, both point and aggregate ones, without flooding the network. At the same time, it manages to preserve the hierarchical nature of data. These characteristics are maintained even after sudden shifts in the workload.
An AAA architecture extension for providing differentiated services to mobile IP users

Differentiated services (DiffServ) are not yet fully adapted and integrated with mobile environments, especially when mobile IP is used as the mobility management protocol in the Internet. When a mobile node visits a foreign network and requests services based on service level agreements (SLAs), the DiffServ Internet service providers (ISPs) must care about how to charge the mobile user. In addition, SLAs have to be renegotiated between home/foreign links and their ISPs. Authentication, authorization and accounting (AAA) procedures must be provided. This paper proposes a concept to combine the service location protocol and the mobile IP AAA based architecture. It is independent of the availability of a foreign agent (FA) and works for IPv4 or IPv6 in a uniform manner. The architecture supports mobile telephony over packet-based IP networks without requiring GSM-like mobility management and accounting schemes.
Toward a Model of Brand Creation for International Large-Scale Sporting Events: The Impact of Leadership, Context, and Nature of the Event

The purpose of this study was to develop a model of brand creation for one-off large-scale sporting events. A case study of the 2005 Montreal FINA (Federation Internationale de Natation) World Championships highlighted the importance of the leadership group (which must include individuals with political/networking, business/management, and sport/event skills), the context, and the nature of the event for creating the event’s brand. The importance of each aspect is suggested to vary depending on the situation. For example, the lack of an initial event brand will result in the leadership group having the greatest impact on the event’s brand creation process. Findings also highlighted differing communication paths for internal and external stakeholders. Thus, this study contributes to the literature by focusing on brand creation and its related factors instead of the management and outcomes of a brand.
Accelerated Deformable Part Models on GPUs

Object detection is a fundamental challenge facing intelligent applications. Image processing is a promising approach to this end, but its computational cost is often a significant problem. This paper presents schemes for accelerating the deformable part models (DPM) on graphics processing units (GPUs). DPM is a well-known algorithm for image-based object detection, and it achieves high detection rates at the expense of computational cost. GPUs are massively parallel compute devices designed to accelerate data-parallel compute-intensive workload. According to an analysis of execution times, approximately 98 percent of DPM code exhibits loop processing, which means that DPM could be highly parallelized by GPUs. In this paper, we implement DPM on the GPU by exploiting multiple parallelization schemes. Results of an experimental evaluation of this GPU-accelerated DPM implementation demonstrate that the best scheme of GPU implementations using an NVIDIA GPU achieves a speed up of 8.6x over a naive CPU-based implementation.
Realtime 3D segmentation for human-robot interaction

We present a real-time algorithm that segments unstructured and highly cluttered scenes. The algorithm robustly separates objects of unknown shape in congested scenes of stacked and occluded objects. The model-free approach finds smooth surface patches, using a depth image from a Kinect camera, which are subsequently combined to form highly probable object hypotheses. Coplanarity and curvature matching is used to recombine surfaces separated by occlusion. The real-time capabilities are proven and the quality of the algorithm is evaluated on a benchmark database. Advantages compared to existing approaches as well as weaknesses are discussed.
Exploiting Code-Redundancies in ECOC for Reducing its Training Complexity using Incremental and

We study an approach for speeding up the training of error-correcting output codes (ECOC) classifiers. The key idea is to avoid unnecessary computations by exploiting the overlap of the different training sets in the ECOC ensemble. Instead of re-training each classifier from scratch, classifiers that have been trained for one task can be adapted to related tasks in the ensemble. The crucial issue is the identification of a schedule for training the classifiers, which maximizes the exploitation of the overlap. For solving this problem, we construct a classifier graph in which the nodes correspond to the classifiers, and the edges represent the training complexity for moving from one classifier to the next in terms of the number of added training examples. The solution of the Steiner Tree problem is an arborescence in this graph, which describes the learning scheme with the minimal total training complexity. We experimentally evaluate the algorithm with Hoeffding trees, as an example for incremental learners, where the classifier adaptation is trivial, and with SVMs, where we employ an adaptation strategy based on adapted caching and weight re-use, which guarantees that the learned model is the same as per batch learning.