Detecting Manipulations in Video

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future

LPS-responsive beige-like anchor gene mutation associated with possible bronchiolitis obliterans organizing pneumonia associated with hypogammaglobulinemia and normal IgM phenotype and low number of B

LPS-Responsive Beige-like Anchor (LRBA) deficiency is a disease which has recently been described in a group of patients with common variable immunodeficiency (CVID) in association with autoimmunity and/or inflammatory bowel disease (IBD)-like phenotype. We here describe a 10-year-old boy who experienced recurrent infections, mainly in the respiratory system, associated with thrombocytopenia and anemia. Immunological workup showed low numbers of B cells and low IgG, but normal IgM levels. In spite of therapeutic doses of antibiotics, antivirals, and antifungal agents, in addition to immunoglobulin replacement therapy, he developed disseminated involvement of both lungs with peripheral nodules; transbronchial lung biopsy revealed possible bronchiolitis obliterans organizing pneumonia (BOOP). Combined homozygosity mapping and exome sequencing identified a homozygous LRBA mutation in this patient (p.Asp248Glufs * 2). Such clinical and immunological findings have not been described to date and illustrate the broad and variable clinical phenotype of human LRBA deficiency. © 2016 Tehran University of Medical Sciences. All rights reserved

Consensus Middle East and North Africa Registry on Inborn Errors of Immunity

Background: Inborn errors of immunity (IEIs) are a heterogeneous group of genetic defects of immunity, which cause high rates of morbidity and mortality mainly among children due to infectious and non-infectious complications. The IEI burden has been critically underestimated in countries from middle- and low-income regions and the majority of patients with IEI in these regions lack a molecular diagnosis. Methods: We analyzed the clinical, immunologic, and genetic data of IEI patients from 22 countries in the Middle East and North Africa (MENA) region. The data was collected from national registries and diverse databases such as the Asian Pacific Society for Immunodeficiencies (APSID) registry, African Society for Immunodeficiencies (ASID) registry, Jeffrey Modell Foundation (JMF) registry, J Project centers, and International Consortium on Immune Deficiency (ICID) centers. Results: We identified 17,120 patients with IEI, among which females represented 39.4%. Parental consanguinity was present in 60.5% of cases and 27.3% of the patients were from families with a confirmed previous family history of IEI. The median age of patients at the onset of disease was 36 months and the median delay in diagnosis was 41 months. The rate of registered IEI patients ranges between 0.02 and 7.58 per 100,000 population, and the lowest rates were in countries with the highest rates of disability-adjusted life years (DALY) and death rates for children. Predominantly antibody deficiencies were the most frequent IEI entities diagnosed in 41.2% of the cohort. Among 5871 patients genetically evaluated, the diagnostic yield was 83% with the majority (65.2%) having autosomal recessive defects. The mortality rate was the highest in patients with non-syndromic combined immunodeficiency (51.7%, median age: 3.5 years) and particularly in patients with mutations in specific genes associated with this phenotype (RFXANK, RAG1, and IL2RG). Conclusions: This comprehensive registry highlights the importance of a detailed investigation of IEI patients in the MENA region. The high yield of genetic diagnosis of IEI in this region has important implications for prevention, prognosis, treatment, and resource allocation

Theoretical error analysis and validation in numerical solution of two-dimensional linear stochastic Volterra-Fredholm integral equation by applying the block-pulse functions

In this paper, we introduce an efficient method based on two-dimensional block-pulse functions (2D-BPFs) to approximate the solution of the 2D-linear stochastic Volterra–Fredholm integral equation. Also, we present convergence analysis of the proposed method. Illustrative examples are included to demonstrate the validity and applicability of the proposed method

Golf performance enhancement by means of ‘real-life neurofeedback’ training based on personalized event-locked EEG profiles

Contains fulltext : 73297.pdf (publisher's version ) (Closed access)Background. This study reports on a new method for golf performance enhancement employing personalized real-life neurofeedback during golf putting. Method. Participants (n = 6) received an assessment and three real-life neurofeedback training sessions. In the assessment, a personal event-locked electroencephalographic (EEG) profile at FPz was determined for successful versus unsuccessful putts. Target frequency bands and amplitudes marking optimal prefrontal brain state were derived from the profile by two raters. The training sessions consisted of four series of 80 putts in an ABAB design. The feedback in the second and fourth series was administered in the form of a continuous NoGo tone, whereas in the first and third series no feedback was provided. This tone was terminated only when the participants EEG met the assessment-defined criteria. In the feedback series, participants were instructed to perform the putt only after the NoGo tone had ceased. Results. From the personalized event-locked EEG profiles, individual training protocols were established. The interrater reliability was 91%. The overall percentage of successful putts was significantly larger in the second and fourth series (feedback) of training compared to the first and third series (no feedback). Furthermore, most participants improved their performance with feedback on their personalized EEG profile, with 25% on average. Conclusions. This study demonstrates that the “zone” or the optimal mental state for golf putting shows clear recognizable personalized patterns. The learning effects suggest that this real-life approach to neurofeedback improves learning speed, probably by tapping into learning associated with contextual conditioning rather than operant conditioning, indicating perspectives for clinical applications.8 p

Developing an integrated model for evaluating R&D organizations’ performance: combination of DEA-ANP

Assessing the performance of the Research and development (R&D) organizations to achieve higher productivity, growth, and development is always a critical necessity. Therefore, developing a more accurate model to evaluate the performance is always required. For this purpose, this study is aimed at developing a decision-making model for evaluating R&D performance. The model comes up with determining the most proper evaluative criteria for assessing R&D organizations. Then, it integrates Data Envelopment Analysis (DEA) with Analytical Network Process (ANP) to assess R&D performance. This paper is aimed to develop an integrated model for evaluating R&D performance. The findings of the study show that the DEA-ANP model is an accurate and acceptable model for evaluating R&D organizations’ performance

An integrated fuzzy carbon management-based model for suppliers' performance evaluation and selection in green supply chain management

Assessing suppliers based on green focused attributes is a critical issue recently implemented by industries due to increasing community knowledge about global warming and climate change. Carbon management is a new branch of green supplier selection which focuses on other aspects of environmental issues. This study integrates a new fuzzy modification of Analytical Hierarchy Process (AHP) known as Fuzzy Preference Programming (FPP) with Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) to assess suppliers’ performance with respect to carbon management criteria. Four dimensions and twelve criteria have been selected based on previous studies and experts’ opinions. Linguistic variables are employed to gather the experts’ opinions about the importance degree of the dimensions and corresponding attributes. Subsequently, the importance weights of each dimension and its corresponding criteria are computed by using FPP. The performance ratings of the suppliers based on the determined criteria are collected under a fuzzy environment using linguistic variables. Then, FVIKOR is applied to obtain the overall environmental performance with respect to carbon management attributes. Through a case study in a textile company, the performance scores of its suppliers were elicited in accordance with this procedure. Finally, validation and managerial implications show that the developed model is robust and applicable