Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse random encoder for age invariant face recognition

Identifying people with age variation is one of the challenging tasks in face recognition since aging changes, e.g. facial shape and increasing wrinkle. In this paper, we propose to learn age-invariant features by transforming face images taken in a different age to uniformly random faces through sparse auto-encoder neural networks. The aim is to build a reference set of sparse random encoders where each one is trained on images in a different age for the same person with random faces as the target values to learn age-free high-level features representation. By using random faces, the learned representation highlights precise features that are invariant across time at the neurons in the hidden layer. The proposed method evaluated on large-scale dataset known as Cross-Age Celebrity Dataset (CACD), it includes more than 160,000 face images of 2,000 different celebrities. The experiments show that the proposed method outperforms the state-of-the-art approaches on handling age variations.
Remodeling of human foot using chain code for designing special shoes

Human foot consisted of some parts that work in an integrated system that allows the body to stand and walk. The problems that disturb these functions can be caused by accident or disability since birth. There are some treatmens that allow some patients to walk with ease by using special footwear or shoes. This special shoes need to have the size and shape of the feet measured correctly. And in some cases, the patient couldnot be asked to lay their feet on the printed board. In this case, the use of two pose image based measurement can help. Moreover, the 3D reconstruction using both images is presented.
Rescaling low‐carbon transformations: Towards a relational ontology

Scale is an emergent theme in current scientific and policy debates on low‐carbon urban transformations. Yet notions of scale employed in such contexts are typically based on linear and hierarchical ontologies, and miss out on the long‐standing development of more nuanced conceptions of scale within Human Geography. This paper aims to advance a relational understanding of scale in the analysis and evaluation of low‐carbon urban initiatives (LCUIs). We wish to lay the path towards an innately geographical conceptualisation of low‐carbon urban transformations more generally, in which cities are not seen as rigid and passive physical containers for decarbonisation initiatives, but rather as key nodes within vibrant socio‐technical networks operating across multiple material sites. Using a case study of the transnational and translocal REACH (Reduce Energy use And Change Habits) project funded by the European Union as illustration, we argue that low‐carbon urban transformations are immanently constituted of three sets of relational processes across scale, involving (1) politicisation, (2) enrolment and (3) the hybridisation of human and material agencies.
Humanoid Walking Robot

Our project is focused on research and development of the application of Hydro Muscles to biologically inspired humanoid robots. Our team designed, researched, and developed a bio-inspired, bipedal walking robot to simulate the human gait cycle. The walking motion is actuated by Hydro Muscles, which are soft artificial muscles that are driven pneumatically or hydraulically to contract and expand longitudinally. These artificial muscles were were modeled to match mass scaled actuation of human muscles on a lower limb skeletal model to create a biologically authentic gait. Further extensions of this project would explore this robot’s potential for clinical, prosthetic, military defense, and other applications.
Task Scheduling of Real-time Systems on Multi-Core Architectures

This paper proposes a hybrid scheduling method for real-time system on homogeneous multi-core architecture. To make the best of the available parallelism in these systems, first an application is partitioned into some parallel tasks as much as possible. Then the parallel tasks are dispatched to different cores, so as to execute in parallel. In each core, real-time applications can run concurrently with non-real-time applications. The scheduling architecture uses a two-level scheduling scheme. At the top level, a sporadic server is assigned to each scheduling policy. Each sporadic server is used to schedule the dispatched tasks according to its scheduling policy. At the bottom level, a rate-monotonic OS scheduler is adopted to schedule the top level sporadic servers.
A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping

The main challenges that marine heterogeneous data integration faces are the problem of accurate schema mapping between heterogeneous data sources. In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method basing on multianalyzer machine learning model. The multianalyzer analysis the learning results comprehensively, and a fuzzy comprehensive evaluation system is introduced for output results' evaluation and multi factor quantitative judging. Finally, the data mapping comparison experiment on the East China Sea observing data confirms the effectiveness of the model and shows multianalyzer's obvious improvement of mapping error rate.
Abstract—information and Communication Technologies (icts) Can Enhance the Knowledge Sharing by Lowering Temporal and Spatial Barriers between Prosumers and Enterprises, and Improving Access to Prosumers' Knowledge for Enterprises. a Major Challenge for Enterprises Involves Investing

in the appropriate ICTs that help facilitate prosumers' knowledge engagement and knowledge transfer. The purpose of the paper is to indicate which ICTs are currently used and expected to be used by prosumers for knowledge sharing. The reported outcomes are the result of a questionnaire survey that yielded responses from 783 Polish and 171 UK based prosumers. The results indicate the primary ICT choices for use and expected use by Poland and UK based prosumers revealing important differences between these countries. The mobile applications being favored amongst the UK respondents whereas the dedicated enterprise website is the favored ICT amongst Polish respondents. Further, the variety of ICTs provided by enterprises may be too limiting to promote the type of knowledge sharing and communications expected to reassure the prosumers.
Building Chinese Walls in Standard Unix

The set-user-id facility in Unix can be used to form the basis for the implementation of a wide variety of diierent security policies in Unix. We show how the Chinese Wall security policy can be implemented using this facility. The approach is not appropriate for security critical applications: it serves to illustrate that it can be done in a rather simple way, and may be useful for less critical applications. Our technique also provides an approach to implementing dynamic segregation of duties in Unix.
Microstructural Integrity of Cerebral Fiber Tracts in Hereditary Spastic Paraparesis with SPG11 Mutation

BACKGROUND AND PURPOSE: ARHSP-TCC is characterized by progressive leg spasticity, ataxia, and cognitive dysfunction. Although mutations in the human SPG11 gene were identified as responsible for ARHSP-TCC, the cerebral fiber integrity has not been assessed systemically. The objective of this study was to assess cerebral fiber integrity and its clinical significance in patients with ARHSP-TCC. MATERIALS AND METHODS: Five patients from 2 families who were clinically and genetically confirmed to have ARHSP-TCC were examined by neuropsychological evaluation and DSI of the brain. We performed voxel-based GFA analysis for global white matter evaluation, tractography-based analysis for tract-to-tract comparisons, and tract-specific analysis of the CST to evaluate microstructural integrity along the axonal direction. RESULTS: The neuropsychological evaluation revealed widespread cognitive decline across all domains. Voxel-based analysis showed global reduction of GFA in the cerebral white matter. Tractography-based analysis revealed a significant reduction of the microstructural integrity in all neural fiber types, while commissure and association fibers had more GFA reduction than projection fibers (P < .00001). Prefrontal and motor portions of the CC were most severely affected among all fiber tracts (P < .00001, P = .018). Tract-specific analysis of the CST validated a “dying-back” phenomenon (R2 = 0.68, P < .00001). CONCLUSIONS: There was a characteristic gradation in the reduction of microstructural integrity among fiber types and within the CC in patients with the SPG11 mutation. The dying-back process in CST might explain the pathogenic mechanisms for ARHSP-TCC.
Designing a bridge program for internationally educated accountants in an era of resource constraints

ABSTRACT Internationally educated professionals (IEPs) encounter significant barriers to entry to their professions upon immigrating. Obtaining education in the new country is an effective means of improving their positions. While universities emphasize the need to attract and serve adult learners and a more diverse clientele, the specific needs of immigrants are rarely reflected in institutional policies. I discuss our pilot program for internationally educated accountants (IEAs), its elements, and best practices. Our program was very successful, but may be beyond the reach of many universities due to financial or personnel constraints. I therefore include a cost/benefit framework to analyze each program feature. With this information, institutions may be able to partially implement a program, maximizing the benefit given their resources. Furthermore, some of the program elements could also be useful for international students, domestic students who will work alongside international colleagues, or domestic students who will work internationally themselves.
Exploring the Mechanism Responsible for Cellulase Thermostability by Structure-Guided Recombination

Cellulases from Bacillus and Geobacillus bacteria are potentially useful in the biofuel and animal feed industries. One of the unique characteristics of these enzymes is that they are usually quite thermostable. We previously identified a cellulase, GsCelA, from thermophilic Geobacillus sp. 70PC53, which is much more thermostable than its Bacillus homolog, BsCel5A. Thus, these two cellulases provide a pair of structures ideal for investigating the mechanism regarding how these cellulases can retain activity at high temperature. In the present study, we applied the SCHEMA non-contiguous recombination algorithm as a novel tool, which assigns protein sequences into blocks for domain swapping in a way that lessens structural disruption, to generate a set of chimeric proteins derived from the recombination of GsCelA and BsCel5A. Analyzing the activity and thermostability of this designed library set, which requires only a limited number of chimeras by SCHEMA calculations, revealed that one of the blocks may contribute to the higher thermostability of GsCelA. When tested against swollen Avicel, the highly thermostable chimeric cellulase C10 containing this block showed significantly higher activity (22%-43%) and higher thermostability compared to the parental enzymes. With further structural determinations and mutagenesis analyses, a 310 helix was identified as being responsible for the improved thermostability of this block. Furthermore, in the presence of ionic calcium and crown ether (CR), the chimeric C10 was found to retain 40% residual activity even after heat treatment at 90°C. Combining crystal structure determinations and structure-guided SCHEMA recombination, we have determined the mechanism responsible for the high thermostability of GsCelA, and generated a novel recombinant enzyme with significantly higher activity.
FEATURE SELECTION AND CLASSIFICATION APPROACH FOR SENTIMENT ANALYSIS

Sentiment analysis and Opinion mining has emerged as a popular and efficient technique for information retrieval and web data analysis. The exponential growth of the user generated content has opened new horizons for research in the field of sentiment analysis. This paper proposes a model for sentiment analysis of movie reviews using a combination of natural language processing and machine learning approaches. Firstly, different data pre-processing schemes are applied on the dataset. Secondly, the behaviour of two classifiers, Naive Bayes and SVM, is investigated in combination with different feature selection schemes to obtain the results for sentiment analysis. Thirdly, the proposed model for sentiment analysis is extended to obtain the results for higher order n-grams.
Approximating signals supported on graphs

In this paper, we introduce the concept of smoothness for signals supported on the vertices of a graph. We provide theoretical explanations when and why the Laplacian eigenbasis can be regarded as a meaningful “Fourier” transform of such signals. Moreover, we analyze the desired properties of the underlying graphs for better compressibility of the signals. We verify our theoretical work by experiments on real world data.
Streamlining and Understanding Curation with Vizier

Data curation (aka wrangling and cleaning) is a critical stage in data science in which raw data is structured, validated, and repaired. Data validation and repair establish trust in analytical results, while appropriate structuring streamlines analytics. Unfortunately, even with advances in automated tools (e.g., Oracle's Data Guide and Trifacta's Wrangler), wrangling is still a major bottleneck in data exploration. Traditionally, curation has been carried out as a pre-processing task: after all data are selected for a study (or application), they are cleaned and loaded into a database or data warehouse. This is problematic because while some cleaning constraints can be easily defined (e.g., checking for valid attribute ranges), others are only discovered as one analyzes the data. Furthermore, as domain experts integrate data sets to test their hypotheses, erroneous data points identified when a data set was analyzed in isolation may actually turn out to be important features. Figure 1: The plot on the top shows how the number of trips varies over 2011 and 2012. While the variation is similar for the two years, there are clear outliers, including large drops in August 2011 and in October 2012. These are not errors, but in fact correspond to hurricanes Sandy and Irene, as shown by the wind speed plot on the bottom. Consider, for example, taxis in New York City. 1 Every day, there are over 500,000 taxi trips transporting about 600,000 people from Manhattan to different parts of the city. 2 Through the meters installed in each vehicle, the Taxi & Limousine Commission (TLC) captures detailed information about trips, including: GPS readings for pickup and drop-off locations, pickup and drop-off times, fare, and tip amount. These data have been used in several projects to understand different aspects of the city, from creating mobility models and analyzing the benefits and drawbacks of ride sharing, to detecting gentrification. In a recent study [6], we investigated quality issues in the taxi data. We found invalid values such as negative mile and fare values , as well as trips that started or ended in rivers or outside of the US. These are clearly errors in the data. Other issues are more nuanced. An example is a fare with a tip of US$938.02 (the maximum tip value for the 2010 dataset). While this could have be an error in the data acquisition or in the credit card information, it could also be the case …
Reduction of Background Computations in Block-Matching Motion Estimation(Video/Image Coding)( Applications and Implementations of Digital Signal Processing)

A new algorithm and architecture to eliminate redundant operations in block-matching (BM) motion estimation is proposed. The key step of this work is to use binary-matching to define image regions with the static background content and then exclude these regions from the actual motion estimation. According to experiments, the approach maintains the highest PSNR, while making as half as less computations in comparison to the adaptive BM or 1/8 of the computations required by the full-search BM. An implementation scheme is outlined. key words: motion estimation, video coding, computational complexity, hardware architecture, adaptive processing
Automatic Phoneme Segmentation Using Auditory Attention Features

Segmentation of speech into phonemes is beneﬁcial for many spoken language processing applications. Here, a novel method which uses auditory attention features for detecting phoneme boundaries from acoustic signal is proposed. The auditory attention model can successfully detect salient audio events/sounds in an acoustic scene by capturing changes that make such salient events perceptually different than their neighbours. There-fore, it naturally offers an effective solution for segmentation task. The proposed phoneme segmentation method does not require transcription or acoustic models of phonemes. When evaluated on TIMIT, the proposed method is shown to successfully predict phoneme boundaries and outperform the recently published text-independent phoneme segmentation methods [1, 2].
Soft Set Based Feature Selection Approach for Lung Cancer Images

Lung cancer is the deadliest type of cancer for both men and women. Feature selection plays a vital role in cancer classification. This paper investigates the feature selection process in Computed Tomographic (CT) lung cancer images using soft set theory. We propose a new soft set based unsupervised feature selection algorithm. Nineteen features are extracted from the segmented lung images using gray level co-occurence matrix (GLCM) and gray level different matrix (GLDM). In this paper, an efficient Unsupervised Soft Set based Quick Reduct (SSUSQR) algorithm is presented. This method is used to select features from the data set and compared with existing rough set based unsupervised feature selection methods. Then K-Means and Self Organizing Map (SOM) clustering algorithms are used to cluster the data. The performance of the feature selection algorithms is evaluated based on performance of clustering techniques. The results show that the proposed method effectively removes redundant features.
ow stable is the core discussion network ?

Researchers have paid increasing attention to the core discussion network, the set of people we turn to when discussing important matters. Because the core discussion network is theorized to be composed of people’s closest ties, not fleeting acquaintances, it is expected to be largely stable, evolving slowly over the span of people’s lives. However, recent studies have shown that networks are strongly affected by the contexts in which people interact with others, and as people experience life course transitions, they also often enter new contexts – school, college, work, marriage, and retirement. We ask whether, as actors enter new social contexts, the core discussion network remains stable or changes rapidly. Based on original, longitudinal, qualitative and quantitative data on the experience of first-year graduate students in three academic departments in a large university, we examine the stability of the core discussion network over the first 6 and 12 months in this new context. We test four competing hypotheses that focus on strength of ties, new opportunities, obligations, and routine activity and predict, respectively, stasis, expansion, shedding, and substitution. We find that the core discussion network changes remarkably quickly, with little or no lag, and that it appears to do so because both the obligations that people face and the routine activities they engage in are transformed by new institutional environments. Findings suggest that core discussion network may be less a “core” network than a highly contextual support network in which people are added and dropped as actors shift from environment to environment. © 2014 Elsevier B.V. All rights reserved.
Arguments in OWL: A Progress Report

In previous work, we presented an RDFS ontology, based on the Argument Interchange Format (AIF), for describing arguments and argument schemes. We also implemented a pilot Web-based system, called ArgDF, for authoring and querying argument structures represented in RDF. In this paper, we discuss some of the limitations of our earlier reification of the AIF. We then present a new ontology which takes advantage of the higher expressive power of OWL. We demonstrate how this enables the use of automated Description Logic reasoning over argument structures. In particular, OWL reasoning enables significantly enhanced querying of arguments through automatic scheme classifications, instance classification, and inference of indirect support in chained argument structures.
ONTOLOGY POPULATION : AN APPLICATION FOR THE E-TOURISM DOMAIN

The Semantic Web aims to extend the current Web standards and technologies so that the semantics of Web contents is machine processable. For the Semantic Web vision to become real, methods and mechanisms that assist in the creation of an initial pool of semantically described Web resources have been developed. However, these methods suffer from some problems such as lack of scalability and automation. Besides, most of them focus on English language resources and the cost of initial requirements is too high. In order to solve these drawbacks, this paper proposes a methodology for extracting semantic content from textual web documents to automatically instantiate a domain ontology (i.e., ontology population). In a first stage, the system obtains, through the GATE framework, a set of semantic annotations which are considered as ontology instance candidates. In a second stage, the semantic ambiguities are solved, and the annotations are related with their corresponding ontological entities. The methodology has been tested in a tourism domain corpus and the results of the validation process seem promising in terms of precision and recall.
Feature Selection In Biological Models

One of the most difficult problems with running classical Newtonian simulations of large biophysical systems is the lack of a systematic way to understand the resulting trajectories. Most common approaches include viewing the trajectories or plotting some key parameter as a function of time. This methodology works because in most biological systems, only a few things change when going from inactive to active regimes. However, the question arises, how do we know what features/degrees of freedom are relevant within in the system and more importantly how can we objectively ensure that we do not miss anything? This problem is a variant of feature selection and decision boundary identification within machine learning literature and has been significantly studied in the past under the umbrella of supervised machine learning. In this project, I intend to show that the Gini Importance used in building decision boundaries for tree classifiers can also be used to efficiently find important features/degrees of freedom in biological simulations.