Miracle-dz/UniFAR-FTUs huggingface dataset:
Face Recognition Based on Human Visual Perception Theories and Unsupervised ANN

The face recognition problem has been faced for more than 30 years. Although a lot of research has been done, much more research is and will be required in order to end up with a robust face recognition system with a potential close to human performance. Currently face recognition systems, FRS, report high performance levels, however achievement of 100% of correct recognition is still a challenge. Even more, if the FRS must work on noncooperative environment its performance may decrease dramatically. Non-cooperative environments are characterized by changes on; pose, illumination, facial expression. Therefore FRS for non-cooperative environment represents an attractive challenge to researchers working on the face recognition area. Most of the work presented in the literature dealing with the face recognition problem follows an engineering approach that in some cases do not incorporate information from a psychological or neuroscience perspective. It is our interest in this material, to show how information from the psychological and neuroscience areas may contribute in the solution of the face recognition problem. The material covered in this chapter is aimed to show how joint knowledge from human face recognition and unsupervised systems may provide a robust alternative compared with other approaches. The psychological and neuroscience perspectives shows evidence that humans are deeply sensible to the face characteristic configuration, but the processing of this configuration is restricted to faces in a face-up position (Thompson, 1980), (Gauthier, 2002). This phenomenon suggests that the face perception process is a holistic configurable system. Although some work has been done in these areas, it is still uncertain, how the face feature extraction processes is achieved by a human being. An interesting case is about newborn face feature extraction. Studies on newborns demonstrate that babies perceive a completely diffuse world, and their face perception and recognition is based on curves and lines from the face (Bower, 2001), (Johnson, 2001), (Nelson, 2001), (Quinn et al., 2001) and (Slater A. & Quinn, 2001). Nowadays, there exists some research work on face recognition that has intended to incorporate psychological and neuroscience perspectives (Blanz & Vetter, 2003), (Burton et O pe n A cc es s D at ab as e w w w . in te ch w eb . o rg
NetworkRepository: A Graph Data Repository with Visual Interactive Analytics

Network Repository (NR) is the first interactive data repository with a web-based platform for visual interactive analytics. Unlike other data repositories (e.g., UCI ML Data Repository, and SNAP), the network data repository (networkrepository.com) allows users to not only download, but to interactively analyze and visualize such data using our web-based interactive graph analytics platform. Users can in real-time analyze, visualize, compare, and explore data along many different dimensions. The aim of NR is to make it easy to discover key insights into the data extremely fast with little effort while also providing a medium for users to share data, visualizations, and insights. Other key factors that differentiate NR from the current data repositories is the number of graph datasets, their size, and variety. While other data repositories are static, they also lack a means for users to collaboratively discuss a particular dataset, corrections, or challenges with using the data for certain applications. In contrast, we have incorporated many social and collaborative aspects into NR in hopes of further facilitating scientific research (e.g., users can discuss each graph, post observations, visualizations, etc.).
Metabolic Imaging of Head and Neck Cancer Organoids

Head and neck cancer patients suffer from toxicities, morbidities, and mortalities, and these ailments could be minimized through improved therapies. Drug discovery is a long, expensive, and complex process, so optimized assays can improve the success rate of drug candidates. This study applies optical imaging of cell metabolism to three-dimensional in vitro cultures of head and neck cancer grown from primary tumor tissue (organoids). This technique is advantageous because it measures cell metabolism using intrinsic fluorescence from NAD(P)H and FAD on a single cell level for a three-dimensional in vitro model. Head and neck cancer organoids are characterized alone and after treatment with standard therapies, including an antibody therapy, a chemotherapy, and combination therapy. Additionally, organoid cellular heterogeneity is analyzed quantitatively and qualitatively. Gold standard measures of treatment response, including cell proliferation, cell death, and in vivo tumor volume, validate therapeutic efficacy for each treatment group in a parallel study. Results indicate that optical metabolic imaging is sensitive to therapeutic response in organoids after 1 day of treatment (p<0.05) and resolves cell subpopulations with distinct metabolic phenotypes. Ultimately, this platform could provide a sensitive high-throughput assay to streamline the drug discovery process for head and neck cancer.
1 Fault Detection and Isolation and Fault Tolerant Control

The main objective of the Fault Detection and Isolation (FDI) research area, widely addressed from several points of view since the early ’80s, is to study methodologies for identifying and exactly characterizing possible incipient faults arising in the plant. As commonly referred to in the literature on this topic (see Chen J. and Patton R.J., Robust model-based fault diagnosis for dynamic systems, Kluwer academic publishers, Boston 1999), the term fault detection is used to describe the problem of making a binary decision either that something has gone wrong or that everything is fine. This is usually achieved designing a dynamical system (usually referred to as filter) which, by processing input/output data, is able to detect the presence of an incipient fault and eventually to precisely isolate it generating the so-called residual signals. The phase of fault detection and isolation is then usually instrumental in order to carry out the phase of control reconfiguration which aims to achieve fault tolerance. The kind of reconfiguration to be taken strongly depends on the severity of the fault affecting the control system: it may consist of shutting down the control system in case of severe faults or, in a less catastrophic scenario, to adopt small reconfigurations in the control structure or in the controller tuning in order to preserve pre-determinate performances also in presence of faults whose effect is “light”. The problem of FDI and Fault Tolerant Control (FTC) is an important problem to deal with, since faults in sensors, actuators and components are usually associated to increasing operating costs, off-specification production, line shut-down and possible detrimental environment impact. Besides its importance from a practical point of view, this topic raises very interesting and challenging research directions. In this respect it is worth noting that the detection of the faults, to be useful in practice, should be achieved early by detecting incipient effects associated to the fault before its effect becomes serious. What renders challenging this task is that usually this must be achieved in presence on uncertainties on the model of the controlled plant and in presence of disturbances and measurement noises which make the task of early and robust fault detection difficult to achieve in a reliable way. The presence of uncertainties and disturbances renders, in general, un-effective the use of modelbased detection strategies and raises important problems such as the possibility of generating false alarms, namely detection of faults which are not present at all, or missed diagnosis, namely missing the detection of a fault. There are different ways which can be adopted to detect and isolate the fault. A common way, in some industrial applications, is to use hardware redundancy, which consists of comparing the outputs of identical hardware and perform consistence cross
XAdap: An Adaptive Huffman Coding on Markup Languages

XML documents are used for data exchange and to store large amount of data over the Web. These documents are extremely verbose and require specific compression for efficient transformation. In this paper we are analyzing various existing compressors and propose a new method called XAdap, which uses adaptive Huffman coding. It is based on the principle of extracting data from the document, and grouping it based on semantics. The document is encoded as a sequence of integers, while the data grouping is based on XML tags/attributes/comments. The re-organized data is now compressed by adaptive Huffman coding. We compare the proposed method with other existing (which uses Huffman coding) tools. Performance evaluation shows that XAdap outperforms previously proposed XML specific compression tools.
On Becoming a District of Choice: Implications for Equity Along the United States–Mexico Border

Purpose: Despite the popularity of open enrollment as a school choice mechanism, there is little research on how principals behave in a district-run competitive setting. This study adds to our understanding of how open enrollment policies affect the role of the principal as well as educational equity by examining the roles and behaviors of school principals in an unregulated marketplace of schools. Research Method: This study uses an explanatory sequential mixed methods approach. We first analyze school-level transfer data for school year 2014-2015 and demographic data in order to examine trends such as poverty concentration as well as to identify “winners,” “losers,” and “nonplayers” in the open enrollment marketplace. Since principals are heavily involved in recruitment, student screening, and selection of specialized programs, we interviewed 12 principals to better understand their role in the competitive settings. Findings: We find that some schools have emerged as “winners” in this marketplace, attracting large numbers of transfers without losing many students, while other principals and schools struggle to overcome a negative perception and find a market niche to attract students. Our quantitative analysis indicates a relatively small relationship between open enrollment and increased segregation in the district. District oversight seems to have prevented worsening segregation. However, many principals seek more control on the screening process raising equity concerns if formal regulations are not provided. Implications: These findings have implications for school and district leaders navigating open enrollment plans as a means to increase enrollments and encourage innovation while also maintaining equity.
Enhancement of Gaussian background modelling algorithm for moving object detection & its implementation on FPGA

Gaussian background modelling algorithm is a classic algorithm used for moving object detection in video. This work proposes a technique to enhance the Gaussian background modelling algorithm in order to improve the moving object detection result. Since the potential application scenario requires real-time video processing, a circuit to implement the algorithm is designed on a Xilinx FPGA. The proposed design is of pipeline structure. Experimentation shows that it can perform real-time moving object detection in a live video of 1280×720p@30fps.
Reinventing CSR in Nigeria: Understanding Its Meaning and Theories for Effective Application in the Industry

Diverse understanding of corporate social responsibility (CSR) abounds among scholars and practitioners in Nigeria. The purpose of this chapter is to reinvent CSR in Nigeria through a deeper understanding of the meaning and theories of this nebulous concept for better application in the industry. The qualitative research approach is adopted, relying on critical review of scholarly articles on CSR, website information of selected companies and institutional documents. It was found that there are diverse meanings of CSR in the reviewed literature, but the philanthropic initiatives and corporate donations for social issues are the common CSR practices in Nigeria. Besides, the eight dominant theories of CSR that find relevance for applications in the industry are shareholder/agency, stakeholder, legitimacy, instrumental, social contract, conflict, green and communication theories. The implication of the discourse is that better understanding and application of CSR theories would strengthen conceptual, theoretical and empirical research in the field of CSR. Besides, CSR theories are useful sources of information for practitioners for designing social responsibility policies and practices as well as for providing scholars with sound theoretical framework for academic research.
Visualization of Discussions in Comments of a Blog Entry Using KeyGraph and Comment Scores

KeyGraph is a visualization tool for discovery of relations among text-based data. This paper discusses a new application of KeyGraph for visualization of discussions in the comments of a blog entry in Slashdot. We propose an approach that applies KeyGraph successively to multiple chunks of comments, each chunk having a different range of moderation scores provided by Slashdot. This approach gives a higher number of scenarios with more specific meaning than a common approach that applies KeyGraph to the whole comments at once. Key-Words: weblogs, blogs, comments, discussions, KeyGraph, Slashdot, moderation
A new form of friendship among young adults in Poland: Identification of the friends with benefits phenomenon

Close friendship is one of the most fundamental issues in psychology. It is a universal relationship in terms of which it is possible to describe every human being, since every individual engages in it. The quality of close friendship relationships is one of the essential conditions of mental health. A close friendship generates space in which to fulfill the most fundamental human needs. The aim of this empirical study was to identify a new form of close interpersonal relationship, called “friend with benefits” and defined as friends having sexual relations. The results of the empirical study allow us to explore this phenomenon, discuss differences in the quality and intensity of the friends with benefits relationship between men and women, and draw some important conclusions.
Normative Feedback Effects on Learning a Timing Task

This study investigated the influence of normative feedback on learning a sequential timing task. In addition to feedback about their performance per trial, two groups of participants received bogus normative feedback about a peer group's average block-to-block improvement after each block of 10 trials. Scores indicated either greater (better group) or less (worse group) than the average improvement, respectively. On the transfer test 1 day later, which required producing novel absolute movement times, the better group demonstrated more effective learning than the worse group. These findings add to the mounting evidence that motivational factors affect motor skill learning.
The factorization method for inverse elastic scattering from periodic structures

This paper is concerned with the inverse problem of scattering of time-harmonic elastic waves from rigid periodic structures. We establish the factorization method to identify an unknown diffraction grating profile (periodic surface) from knowledge of the scattered compressional or shear waves measured on a line above the periodic surface. Near-field operators are factorized by selecting appropriate incident waves derived from quasi-periodic half-space Green's tensor to the Navier equation. The factorization method gives rise to a uniqueness result for the inverse scattering problem by utilizing only the compressional or shear components of the scattered field corresponding to all quasi-periodic incident plane waves with a common phase-shift. A number of computational examples are provided to show the accuracy of the inversion algorithms, with emphasis placed on comparing reconstructions from the scattered near field and those from its compressional and shear components.
Adaptive optics observations of LBQS 0108+0028: K-band detection of the host galaxy of a radio-quiet QSO at z ≈ 2

ABSTRA C T We report the first unambiguous detection of the host galaxy of a normal radio-quiet QSO at high redshift in the K band. The luminosity of the host comprises about 35 per cent of the total K-band luminosity. Assuming the average colour of QSOs at z < 2, the host would be about 5 to 6 mag brighter than an unevolved L¬ galaxy placed at z < 2, and 3 to 4 mag brighter than a passively evolved L¬ galaxy at the same redshift. The luminosity of the host galaxy of the QSO would thus overlap with the highest found in radio-loud QSOs and radio galaxies at the same
Sequence and structural requirements for high-affinity DNA binding by the WT1 gene product

The Wilms' tumor suppressor gene, WT1, encodes a zinc finger polypeptide which plays a key role regulating cell growth and differentiation in the urogenital system. Using the whole-genome PCR approach, we searched murine genomic DNA for high-affinity WT1 binding sites and identified a 10-bp motif 5'GCGTGGGAGT3' which we term WTE). The WTE motif is similar to the consensus binding sequence 5'GCG(G/T)GGGCG3' recognized by EGR-1 and is also suggested to function as a binding site for WT1, setting up a competitive regulatory loop. To evaluate the underlying biochemical basis for such competition, we compared the binding affinities of WT1 and EGR1 for both sequences. WT1 shows a 20- to 30-fold-higher affinity for the WTE sequence compared with that of the EGR-1 binding motif. Mutational analysis of the WTE motif revealed a significant contribution to binding affinity by the adenine nucleotide at the eighth position (5'GCGTGGGAGT3') as well as by the 3'-most thymine (5'GCGTGGGAGT3'), whereas mutations in either flanking nucleotides or other nucleotides in the core sequence did not significantly affect the specific binding affinity. Mutations within WT1 zinc fingers II to IV abolished the sequence-specific binding of WT1 to WTE, whereas alterations within the first WT1 zinc finger reduced the binding affinity approximately 10-fold but did not abolish sequence recognition. We have thus identified a WT1 target, which, although similar in sequence to the EGR-1 motif, shows a 20- to 30-fold-higher affinity for WT1. These results suggest that physiological action of WT1 is mediated by binding sites of significantly higher affinity than the 9-bp EGR-1 binding motif. The role of the thymine base in contributing to binding affinity is discussed in the context of recent structural analysis.
Visualizing sequential patterns for text mining

A sequential pattern in data mining is a finite series of elements such as A/spl rarr/B/spl rarr/C/spl rarr/D where A, B, C, and D are elements of the same domain. The mining of sequential patterns is designed to find patterns of discrete events that frequently happen in the same arrangement along a timeline. Like association and clustering, the mining of sequential patterns is among the most popular knowledge discovery techniques that apply statistical measures to extract useful information from large datasets. As out computers become more powerful, we are able to mine bigger datasets and obtain hundreds of thousands of sequential patterns in full detail. With this vast amount of data, we argue that neither data mining nor visualization by itself can manage the information and reflect the knowledge effectively. Subsequently, we apply visualization to augment data mining in a study of sequential patterns in large text corpora. The result shows that we can learn more and more quickly in an integrated visual data-mining environment.
Discovering implicit associations among critical biological entities

We propose an approach to predicting implicit gene-disease associations based on the inference network, whereby genes and diseases are represented as nodes and are connected via two types of intermediate nodes: gene functions and phenotypes. To estimate the probabilities involved in the model, two learning schemes are compared; one baseline using co-annotations of keywords and the other taking advantage of free text. Additionally, we explore the use of domain ontologies to complement data sparseness and examine the impact of full text documents. The validity of the proposed framework is demonstrated on the benchmark data set created from real-world data.
Exploration of the Problematic Twitter, Facebook Uses and Their Relationships with Psychopathological Symptoms Among Facebook Users

Background: Problematic Internet Use (PIU), as well as Problematic Facebook Use (PFU), is a growing concern worldwide. Like Facebook, Twitter is a widely used social networking site. Yet, no study has been conducted on the speciﬁc problematic Twitter use (PTU). Objectives: The main goal of this study was to explore the rate of problematic Twitter and Facebook uses, their relationships and diﬀerences in terms of psychopathological and internet-related variables. Patients and Methods: A sample of 822 Facebook users (55% women) aged from 18 to 29 (Mean = 21.6; SD = 2.8) completed a set of questionnairesassessingTwitter,Facebook,andtheinternetusecharacteristicsandproblematicuses,aswellaspsychopathological symptoms. Results: Among the total sample, approximately 18% of the participants had a PIU. Besides, more than 4% (n = 34) had a PFU with a signiﬁcantlyhigherrateof women(P< 0.01). Twitteruserswererepresentedbyapproximately32% (n=259)of thesample. 21% (n= 55)reportedaPIU,3.1% (n=8)aPTU(n=8),and3.9% (n=10)aPFUwithasigniﬁcantmajorityofwomen(P< 0.05). Multiple-regression analysis revealed signiﬁcant diﬀerences between PTU and PFU, particularly in terms of time spent on Facebook and psychopathological symptoms. Conclusions: PFU was particularly related to depression and anxiety symptoms while PTU was not. Moreover, our results suggest further exploring the speciﬁcity of speciﬁc Social Networking Site (SNS) instead of encompassing them into a general problematic use of the internet or SNS. This exploratory study had limitations and needs to be completed by research focusing on relationships anddiﬀerencesbetweentheseuses. FuturestudiesneedtofocusontherelationshipbetweenproblematicSNSuses,takenseparately, with psychopathology, and especially pathological personality traits, by taking gender into account.
Mapping platforms into a new open science model for machine learning

Abstract Data-centric disciplines like machine learning and data science have become major research areas within computer science and beyond. However, the development of research processes and tools did not keep pace with the rapid advancement of the disciplines, resulting in several insufficiently tackled challenges to attain reproducibility, replicability, and comparability of achieved results. In this discussion paper, we review existing tools, platforms and standardization efforts for addressing these challenges. As a common ground for our analysis, we develop an open science centred process model for machine learning research, which combines openness and transparency with the core processes of machine learning and data science. Based on the features of over 40 tools, platforms and standards, we list the, in our opinion, 11 most central platforms for the research process in this paper. We conclude that most platforms cover only parts of the requirements for overcoming the identified challenges.