Miracle-dz/UniFAR-FTUs huggingface dataset:
Empowerment through advertisements: the social responsible role of depicting images of special needs for normalcy

Media messages are capable of fashioning our perception of reality through the empowerment of distinctive images of people with special needs. Without the existence of special needs representation in advertising, the inclusion of people with special needs in mainstream society could be absent. The act of normalizing distinctions through dignified portrayals of special needs people who are diverse could be an additional function of advertising in addition to bringing people together at the marketplace when selling products. An organization involved in normalizing people with special needs as part of society is doing a noble act that should be encouraged similar to upholding the visibility of a brand. Several objectives could be met through advertising from inculcating positive attitudes to special needs people, encouraging values in people, normalizing special needs people by mainstream society to promoting goodwill to the organization. Without such visible depictions, people with special needs could encounter possible exclusion from society initiated by a negative attitude to them. Values such as compassion and empathy could be communicated through these depictions and inculcated within the audience. These efforts could be part of the organization’s initiative in being socially responsible.
Perception-Driven Motion Control Based on Stochastic Nonlinear Model Predictive Controllers

In mobile robotics, constraints may represent mobility issues, vision uncertainties, or localization uncertainties. In model predictive control (MPC) theory, constraint satisfaction is typically guaranteed through the use of accurate prediction models or robust control. However, although MPC offers a certain degree of robustness to system uncertainties, its deterministic formulation typically renders it inherently inadequate for systematically dealing with uncertainties. Furthermore, the model used by robust controllers is often not updated online. Towards this direction, this paper presents a stochastic nonlinear model predictive control (SNMPC) algorithm for active target tracking. Our goal is to use a SNMPC to penalize the undesired behavior, allowing the robot to converge to the optimal pose in order to observe the target optimally. We perform this by proposing uncertainty prediction models for both the target position estimation and the robot pose estimation. Based on these models, the predictive controller allows the robot to drive at or near its capabilities while respecting the imposed constraints. This paper presents real robot experiments in which the stochastic nonlinear controller provides satisfactory target tracking control.
An energy literacy matrix: a tool for adult and continuing education curriculum design

In this article the authors report on the outcome of a mixed methods study which assessed the renewable energy literacies of adults’ in rural communities across Britain. The research team used a stratified sample survey (n=6000), interviews with householders (n=97), stakeholder interviews (n=7) and one focus group meeting to collect primary data. Data from the study highlighted a series findings: a) there is a clear disjuncture between individuals’ awareness and understanding of energy related matters and government and industry rhetoric; b) there is a public appetite for more reliable (trustworthy) information on renewable energy; c) the public is not well informed about renewable energy debates and government schemes to save energy. Responding to the findings an energy literacy matrix which can be used to plot knowledge of, against, knowledge about renewable energy sources has been developed. The literacy matrix provides a development education tool to focus adult educators’ efforts on raising awareness and understanding of how local, national and global issues affect the everyday lives of individuals and communities.
The Emerging Technology of Big Data

Social media has been become one of the largest platforms on the Internet for people to interact with each other. It can be used as a platform to further connect with consumers. Social media means any form of websites or applications that allows users to create and share information actively or to participate in social networking. People, including students, use social media in all sorts of different ways. One of the growing trends in social media is to start a business online, a form of e-commerce, whereby the social media can serve as a platform or an online retail store for certain products. With the emergence and evolution of the smartphones that are equally competitively produced by Apple, Samsung, mobile phones have become one of the most globally popular social media platforms that provide various service of communication. The power of social networking is very interesting, as it not only serves as a platform for networking with others, but it can also be used as a hub for educational learning. In business practices, companies are able to make use of social media to reach out and connect with their customers in order to ensure consumers of their products maintain a sense of loyalty. Social media consists of unlimited data, called by big data. Big data is defined as a cultural, technological, 2 The Emerging Technology of Big Data and scholarly phenomenon that rests on the interplay of technology and analysis and mythology. These three concepts are linked together when technology maximizes the computation power and algorithmic accuracy to gather, analyze, link and compare large data sets. Analysis works in a way that it draws large data sets to identify the patterns in order to make economic, social, technical, and legal claim. It is a widespread myth that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy. This study emphasized the effect of big data and social media issues for organization redesign of business processes and reengineering.
GSAn: an alternative to enrichment analysis for annotating gene sets

The revolution in new sequencing technologies, by strongly improving the production of omics data, is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze these massive data that are grouped according to a phenotype of interest, methods based on statistical enrichment became a standard in biology. However, these methods synthesize the biological information by a priori selecting the over-represented terms and may suffer from focusing on the most studied genes that represent a limited coverage of annotated genes within the gene set. To address these limitations, we developed GSAn, a novel gene set annotation Web server that uses semantic similarity measures to reduce a priori Gene Ontology annotation terms. The originality of this new approach is to identify the best compromise between the number of retained annotation terms that has to be drastically reduced and the number of related genes that has to be as large as possible. Moreover, GSAn offers interactive visualization facilities dedicated to the multi-scale analysis of gene set annotations. GSAn is available at: https://gsan.labri. fr.
Faculties and Students past Research and Major Accomplishments Outline and Research Objectives

Most of our research has been focused on the higher brain functions of the mammalian central nervous system, in particular, the neural mechanisms of cognitive memory in the primate. The basic motivation of the research can be best explained as follows: Knowledge or experiences are voluntarily recalled from memory by reactivation of their neural representations in the cerebral association cortex. Three questions are central in the understanding of this process: (1) Where are mnemonic representations coded and how are they organized? (2) Which neural processes create the representation? (3) What is the mechanism underlying reactivation of the representation on demand of voluntary recall?
Adaptive output feedback control of uncertain nonlinear systems with input delay and output constraint

This paper proposes an adaptive neural‐network control design for a class of output‐feedback nonlinear systems with input delay and unmodeled dynamics under the condition of an output constraint. A coordinate transformation with an input integral term and a Nussbaum function are combined to solve the problem of the input possessing both time delay and unknown control gain. By utilizing a barrier Lyapunov function and designing tuning functions, the adjustment of multiparameters is handled with a single adaptive law. The uncertainty of the system is approximated by dynamic signal and radial basis function neural networks (RBFNNs). Based on Lyapunov stability theory, an adaptive tracking control scheme is developed to guarantee all the signals of the closed‐loop systems are semiglobally uniformly ultimately bounded, and the output constraint is not violated.
Evaluating the Effect of Pretreatment Parameters on Co-Cr-Mo Alloy Prior to Physical Vapor Deposition Coating

Surface morphology and surface roughness of substrate are important parameters that determine the quality of physical vapor deposition (PVD) coating. Various methods are adopted to address this issue and one of them is by using chemical pretreatment. In the present work, an attempt is made to study the effect of pretreatment parameters, namely temperature and time, using two types of acids on surface morphology of Co-Cr-Mo alloy. The surface roughness Ra is the independent response variable. Experimental results show that higher surface roughness was obtained at temperature of 50˚C and time of 30 seconds when etched using HCl + HNO3 + Acetic acid + H20. However, temperature of acids and time of etching are involving, to obtain the optimum situation of substrate surface prior to PVD coating.
Motivated secrecy: Politics, relationships, and regrets.

Recent work has explored the dynamics of secrecy and its outcomes, but has yet to examine the motivations people have for engaging in secrecy and how such motivations shape the experience of secrecy and its implications. We present a motivational model of secrecy, and test this model in diverse contexts: (a) politics (secret votes in the 2016 United States election), (b) common secrets people keep, and (c) romantic relationships (secrets from partners) across a large sample of participants (total N 1,839). We explored the motivations people have for keeping a secret, and the psychological implications of having a secret for one’s self and relationships. We found that mind wandering to secrets (but not concealing secrets) was associated with feelings of inauthenticity and regret. Moreover, it was secrecy motivated by concern for one’s reputation rather than one’s relationships that predicted these harms of secrecy.
On Whitney-type Characterization of Approximate Differentiability on Metric Measure Spaces

Abstract We study approximately differentiable functions on metric measure spaces admitting a Cheeger differentiable structure. The main result is a Whitney-type characterization of approximately differentiable functions in this setting. As an application, we prove a Stepanov-type theorem and consider approximate differentiability of Sobolev, $BV$ , and maximal functions.
Detecção de casos suspeitos de fraudes em licitações realizadas nos municípios da Paraíba: uma aplicação de técnicas de mineração de dados

This dissertation aims to detect collusive bidding schemes in government procurement auctions during 2005 to 2016 in the municipalities of Paraíba state (Brazil). We use data from Court of Audit of the State of Paraíba and data mining techniques to identify association rules among bidders. The association rules were evaluated considering patterns of cooperative strategies in repeated games, taking account the bid winner likelihood and the bidders competition on average. The finds suggest many cases of bidder collusion, including meals suppliers, cleaning services, rental of vehicles, copiers, stages, chemical toilets, fuel suppliers, automotive parts, sports equipments, medicines and hospital material, consulting in accounting, civil engineering and advertising. We also find several patterns of association between bidders with evidence of competition simulation and concentrated activity in a few municipalities. In general, the results provide important guidelines to support the work of government audition.
A Subgradient Algorithm for Nonlinear Integer Programming

This paper describes a subgradient approach to nonlinear integer programming and, in particular, nonlinear 0-1 integer programming. In this approach, the objective function for a nonlinear integer program is considered as a nonsmooth function over the integer points. The subgradient and the supporting plane for the function are deened, and a necessary and suucient condition for the optimal solution is established, based on the theory of nonsmooth analysis. A new algorithm, called the subgradient algorithm , is developed. The algorithm is in some sense an extension of Newton's method to discrete problems: The algorithm searches for a solution iteratively among the integer points. In each iteration, it generates the next point by solving the problem for a local piecewise linear model. Each local model is constructed using the supporting planes for the objective function at a set of previously generated integer points. A solution is found when either the optimality condition is satissed or an iterate is repeated. In either case, the algorithm terminates in nite steps. The theory and the algorithm are presented. The methods for computing the supporting planes and solving the linear subproblems are described. Test results for a small set of problems are given.
ARLY BLINDNESS RESULTS IN ABNORMAL CORTICOCORTICAL ND THALAMOCORTICAL CONNECTIONS

bstract—Studies in congenitally blind and bilaterally enuleated individuals show that an early loss of sensory driven ctivity can lead to massive functional reorganization. Howver, the anatomical substrate for this functional reorganizaion is unknown. In the present study, we examined patterns f corticocortical and thalamocortical connections in adult possums that had been bilaterally enucleated neonatally, rior to the formation of retinogeniculate and geniculocortial connections. We show that in addition to normal thalamoortical projection patterns from visual nuclei, enucleated nimals also receive input from nuclei associated with the omatosensory (ventral posterior nucleus, VP), auditory (meial geniculate nucleus, MGN), motor (ventrolateral nucleus, L), and limbic/hippocampal systems (anterior dorsal nuleus, AD; and anterior ventral nucleus, AV). Likewise, in ddition to normal corticocortical projections to area 17, biaterally enucleated opossums also receive input from audiory, somatosensory, and multimodal cortex. These aberrant atterns of thalamocortical and corticocortical connections an account for alterations in functional organization oberved in the visual cortex of bilateral enucleated animals, nd indicate that factors extrinsic to the cortex play a large ole in cortical field development and evolution. On the other and, the maintenance of normal patterns of connections in he absence of visual input suggests that there are formidale constraints imposed on the developing cortex that highly estrict the types of evolutionary change possible. © 2006 BRO. Published by Elsevier Ltd. All rights reserved.
Spectrum: fast density-aware spectral clustering for single and multi-omic data

Clustering of single or multi-omic data is key to developing personalised medicine and identifying new cell types. We present Spectrum, a fast spectral clustering method for single and multi-omic expression data. Spectrum is flexible and performs well on single-cell RNA-seq data. The method uses a new density-aware kernel that adapts to data scale and density. It uses a tensor product graph data integration and diffusion technique to reveal underlying structures and reduce noise. We developed a powerful method of eigenvector analysis to determine the number of clusters. Benchmarking Spectrum on 21 datasets demonstrated improvements in runtime and performance relative to other state-of-the-art methods. Contact: christopher.john@qmul.ac. uk
Train track complex of once-punctured torus and 4-punctured sphere

Consider a compact oriented surface $S$ of genus $g \geq 0$ and $m \geq 0$ punctured. The train track complex of $S$ which is defined by Hamenst\"adt is a 1-complex whose vertices are isotopy classes of complete train tracks on $S$. Hamenst\"adt shows that if $3g-3+m \geq 2$, the mapping class group acts properly discontinuously and cocompactly on the train track complex. We will prove corresponding results for the excluded case, namely when $S$ is a once-punctured torus or a 4-punctured sphere. To work this out, we redefinition of two complexes for these surfaces.
A New Object Detection Method in Indoor Scenes Based on Spatial Distance Clustering

3D object detection is one of the important problems in machine vision. With the increasing popularity of depth camera, 3d object detection in point cloud has become a research hotspot of 3D vision. The irregular format of point cloud makes the traditional deep learning method based on image convolution unable to understand and analyze it. In recent years, the deep learning framework as PointNet[1] directly applied to the original structure of point cloud has greatly improved the ability of deep learning network to process point cloud data. This paper depends on the existing point cloud deep learning framework, proposes an object detection method based on semantic segmentation. We use the clustering method based on region growth to detect the objects in indoor scenes. This method has been tested on the Stanford large-scale 3D Indoor Spaces Dataset (S3DIS) Dataset[2] with good results.
Analysis and Design of Functional Device for Vehicular Cloud Computing

Relay technology application becomes prevalent nowadays, as it can effectively extend the communication distance, especially for vehicular networks with a limited communication range. Combined with vehicular cloud (VC), transmission efficiency can be improved by offloading partial data. Hence, designing a vehicle relay algorithm and implementation embedded vehicle device is critical. In this paper, VC is considered to deal with the complexity computation in our proposed system model. Without a loss of generality, an end-to-end vehicle communication with one assisted vehicle is analyzed firstly on a transmission link based on VC. Here, the signal-to-noise ratio (SNR) on the receiving end and link outage probability is obtained to enhance the link reliability. The VC computing helps us further simplify computational complexity. Subsequently, an embedded vehicle-enabled device is designed to achieve the optimal path relay selection in realistic vehicular environments. In the functional device framework, we display an optimal path relay selection algorithm according to the link quality. Finally, the performance of the transmission link on the outage probability related with SNR is verified in the simulation results. Meanwhile, the effect of the relay gain is also analyzed. The application of a vehicle-enabled embedded device could improve the performance of vehicular networks.
TINGKAT KERAMAH LINGKUNGAN ALAT TANGKAP GILL NET DI KECAMATAN NIPAH PANJANG, JAMBI

Kompetisi antar alat tangkap untuk mendapatkan hasil tangkapan yang maksimal, menimbulkan kekhawatiran akan terjadinya over fishing. Oleh sebab itu, alat tangkap ramah lingkungan merupakan acuan dalam penggunaan teknologi dan alat tangkap ikan. Tujuan dari penelitian ini untuk mengetahui tingkat keramah lingkungan dari masing-masing alat tangkap nelayan gill net yang ada di Kelurahan Nipah Panjang 1 pada bulan Februari 2018, berdasarkan kriteria FAO (1995). Metode yang digunakan dalam penelitian ini adalah survei. Teknik pengambilan sampel yaitu purposive sampling dengan responden nelayan yang telah melaut minimal 5 tahun dan kapal yang digunakan minimal berukuran 3 GT. Sampel diambil sebanyak 50% dari masing-masing populasi alat tangkap untuk dianalisis tingkat keramah lingkungannya. Hasil analisis tingkat keramah lingkungan alat tangkap nelayan gill net di Kelurahan Nipah Panjang 1 menunjukkan bahwa gill net kurau termasuk dalam kategori alat tangkap sangat ramah lingkungan dengan nilai sebesar 28. Sedangkan untuk alat tangkap gill net 7 inci, gill net millennium, dan gill net 4 inci termasuk dalam kriteria alat tangkap ramah lingkungan dengan nilai berturut-turut adalah 25.2, 23.8, dan 23.5.
Cybercrime and cybercriminals: A comprehensive study

The increasing expansion and diversification in the strategies and practices of cybercrime has become a difficult obstacle in order both to understand the extent of embedded risks and to define efficient policies of prevention for corporations, institutions and agencies. The present study represents the most comprehensive review of the origin, typologies and developments of Cybercrime phenomenon over the past decade so far. By means of this detailed study, this paper tackles the issue first describing and discussing former different criteria of classification in the field and secondly, providing a broad list of definitions and an analysis of the cybercrime practices. A conceptual taxonomy of cybercrime is introduced and described. The proposal of a classification criterion is used in conjunction with a cybercrime hierarchy derived from the degrees and scale of vulnerability and targets.
The Use of Structured Teaching Strategies to Support Students on the Autism Spectrum to Stay on Task in Mainstream Classrooms

Students on the autism spectrum can find the mainstream classroom environment challenging, and there is a need for effective strategies to support these students within the mainstream context. Staying on task and transitioning independently between tasks have been identified as activities that can be particularly difficult for students on the spectrum. This thesis presents a research project that aimed to develop and evaluate an intervention based on two structured teaching strategies – visual schedules and work systems – which have demonstrated potential in supporting students on the spectrum to stay on task. Whereas other research has mainly evaluated these strategies in special educations or autism specific settings, or outside the classroom, this project examined their use as inclusive, whole-class practices to support students on the autism spectrum in mainstream classrooms. The research presented here involved a mixed-methods design over three distinct phases. In Phase 1, a case-study approach was used to trial the intervention as well as to test and refine research methods for use in the study that followed. In Phase 2, a multiple baseline design was employed to evaluate the use of visual schedules and work systems to support students on the spectrum to stay on task in mainstream settings. Finally, in Phase 3, an online survey and interviews were used to investigate the attitudes of mainstream teachers toward the intervention package. The pilot study established the feasibility of using visual schedules and work systems in mainstream settings, and facilitated the development and refinement of research methods suitable for use in the mainstream classroom. The evaluation of the strategies during Phase 2 demonstrated that their use had a positive and significant impact on the on-task behaviours of students on the spectrum. In Phase 3, teachers who had viewed and trialled the intervention package reported that it was suitable for use in mainstream classrooms. Together, these studies provide evidence that visual schedules and work systems can be effectively implemented by teachers to support students on the spectrum to stay on task in mainstream settings. The research presented here has contributed to efforts to bridge the research-to-practice gap by evaluating these strategies in ecologically valid settings.