Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevention of Identity Leakage and Access Management Using Anony Control-F Attribute Based Encryption

: Network security is needed to protect to protect network. Cloud computing may be a revolutionary computing paradigm, that permits versatile, on-demand, and cheap usage of computing resources, but the data is outsourced to some cloud servers, and varied privacy concerns emerge from it. Numerous schemes Supported the attribute-based committal to writing are projected to secure the cloud storage. However, most work focuses on the data contents privacy and so the access management, whereas less attention is paid to the privilege management and so the identity privacy. Throughout this paper, a semi anonymous privilege management theme Anony Control is projected to handle not entirely the information privacy, but collectively the user identity privacy in existing access control schemes. Anony management decentralizes the central authority to limit the identity run and so achieves semi anonymity. Besides, it collectively generalizes the file access management to the privilege management, by those privileges of all operations on the cloud data is managed in associate passing fine-grained manner. Afterwards, the Anony Control-F is presented, that absolutely prevents the identity outflow and succeeds the whole obscurity. The protection analysis shows that each Anony management and Anony Control-F unit secure beneath the decisional linear Diffie–Hellman assumption, and new performance analysis exhibits the practicability of latest schemes.
Intelligent Detection for Cyber Phishing Attacks using Fuzzy rule-Based Systems

Cyber phishing attacks are increasing rapidly, causing the world economy monetary losses. Although various phishing detections have been proposed to prevent phishing, there is still a lack of accuracy such as false positives and false negatives causing inadequacy in online transactions. This study constructs a fuzzy rule model utilizing combined features based on a fuzzy inference system to tackle the foreseen inaccuracy in online transactions. The importance of the intelligent detection of cyber phishing is to discriminate emerging phishing websites with a higher accuracy. The experimental results achieved an excellent accuracy compared to the reported results in the field, which demonstrates the effectiveness of the fuzzy rule model and the feature-set. The findings indicate that the new approach can be used to discriminate between phishing and legitimate websites. This paper contributes by constructing a fuzzy rule model using a combined effective feature-set that has shown an excellent performance. Phishing deceptions evolve rapidly and should therefore be updated regularly to keep ahead with the changes.
Post‐selection estimation and testing following aggregate association tests

The practice of pooling several individual test statistics to form aggregate tests is common in many statistical applications where individual tests may be underpowered. Although selection by aggregate tests can serve to increase power, the selection process invalidates inference based on the individual test statistics, making it difficult to identify those that drive the signal in follow‐up inference. Here, we develop a general approach for valid inference following selection by aggregate testing. We present novel powerful post‐selection tests for the individual null hypotheses which are exact for the normal model and asymptotically justified otherwise. Our approach relies on the ability to characterize the distribution of the individual test statistics after conditioning on the event of selection. We provide efficient algorithms for computation of the post‐selection maximum likelihood estimates and suggest confidence intervals which rely on a novel switching regime for good coverage guarantees. We validate our methods via comprehensive simulation studies and apply them to data from the Dallas Heart Study, demonstrating that single‐variant association discovery following selection by an aggregate test is indeed possible in practice.
Video Engineering

From the Publisher: A tutorial lifesaver for engineers and technicians alike, this complete update of the top reference in video brings you up to speed on the many advances that have remade the industry over the last few years. "Loaded with real-life examples, Video Engineering helps you with the task at hand, whether it's choosing new equipment, designing a system, or solving a specific technical problem. The most comprehensive and current guidebook available, it provides unsurpassed coverage of digital cameras, receivers, satellite broadcasting, monitors, standards, image quality, audio for video, video on the Internet, plus analog systems, as well.
Mapping 3D Gestural Inputs to Traditional Touchscreen Interface Designs within the Context of Anesthesiology

Gestures are a natural means of every day human-human communication, and with the advances in gestural input technology, there is an opportunity to investigate gestures as a means of communicating with computers and other devices. The primary benefit of gestural input technology is that it facilitates a touchless interaction, so the ideal market demand for this technology is an environment where touch needs to be minimized. The perfect example of an environment that discourages touch are sterile or clean environments, such as operating rooms (ORs). Healthcare-associated infections are a great burden to the healthcare system, and gestural input technology can decrease the number of surfaces, computers, and other devices that a healthcare provider comes in contact with, thus reducing the likelihood of bacterial contamination. The objective of this research was to map 3D gestural inputs to traditional touchscreen interface designs within the context of anesthesiology. An experimental study was conducted to elicit intuitive gestures from users and assess the cognitive complexity of ten typical functions of anesthesia providers. Intuitive gestures were observed in six out of the ten functions without any cognitive complexity concerns. Two functions, of the remaining four, demonstrated a higher-level gesture mapping with no cognitive complexity concerns. Overall, gestural input technology demonstrated promise for the ten functions of anesthesia providers in the operating room, and future research will continue investigating the application of gestural input technology for anesthesiology in the OR.
Modeling and experiments of a soft robotic gripper in amphibious environments

This article presented the optimization parameter of a bidirectional soft actuator and evaluated the properties of the actuator. The systematic simulation was conducted to investigate the effect of the top wedged angle (the angle for the wedged shape of the actuator structure) of the chamber on the bending extent of the actuator when it is deflated. We also investigated the width of the actuator and the material combinations of the two layers with the relation to the deformation performance. A mathematical model was also built to reveal the deformation of the actuator as a function of the geometrical parameters of the inner chambers and the material properties. We quantitatively measured the bending radius and the actuation time of the actuator both in air and under water. Digital particle image velocimetry experiments were conducted under water to observe the flow patterns around the actuator. We found that the top wedged angle has a significant effect on the outward bending of the actuator when it is deflated, and 15° was found to be optimal for bending into a larger gripping space. The result shows that the actuator can deform much easier with a bigger width. Utilizing a soft gripper that was built by mounting four actuators to a three-dimensional-printed rigid support, we found that the prototype can grip objects of different sizes, shapes, and material stiffness in amphibious environments.
Low Voltage Analog to Digital Converter Design in 90nm CMOS

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Louis, Mubaraq and Dave, and everyone else, for dispensing both useful design insigths and enjoyable lunch breaks. The time spent with Peter Haldi, Luca DeNardis and Davide Guermandi during the last year was great. Thanks to Davide and to Luca DeNardis the after-lunch coffee break has become an habit at the center. Davide will be definetely be longed for , being simulateneously one of the best circuit designers I ever met and the most reliable Cadence support that ever appeared at the center. The (l')Abete,Andrea, Fabrizio and Alvise has not only provided a roster of teammates for several unsuccessfull soccer teams, but also a refuge where I could feel less of a stranger. My family and my friends in Italy have never been any farther than when I was still living in their same city. My mother Silvia, my brother Francesco, as well as and all the others, kept me up to date with events across the ocean on an almost daily basis, and made me feel a less drastic departure. And by no means last in importance, my girlfriend Marta. I met her while I was completing the design described in chapter 4. At that time,many testified that the due to underestimated workload, I was as close as I have ever been to becoming a homeless person. She prevented me from rolling down the final steps and moving to People's Park, and became a part of my life I can't do without. I hope I will never have to loose this addiction to her that I developed.
Correction: Interactive Multiple Model Hazard States Prediction for Unmanned Aircraft Systems (UAS) Detect and Avoid (DAA)

This research aims at improving Detect and Avoid (DAA) functions in Unmanned Aircraft Systems (UAS) using multiple model estimation to track maneuvering intruders. This research builds on previous work that used predefined aircraft encounter trajectories. An established encounter model generates the intruder trajectories while multiple models are introduced to improve intruder dynamics estimation. A new method using the Kalman prediction phase inside the Interactive Multiple Model (IMM) algorithm is presented to estimate DAA Hazard States (time to closest point of approach, horizontal miss distance, and vertical separation). The efficiency of the trajectory estimation has direct implication on the estimation of the intruder trajectory in relation to the own aircraft. The methods described in this research can aid a certification authority in determining if a DAA system is sufficient for UAS integration into the National Airspace System.
Sensor Analysis and Application of Arduino based Temperature and Light Intensity Control for Smart Home System

Temperature control system and light intensity control system for smart home are becoming more common in these days. The proposed system is to manage the temperature and the light intensity of a given area based on setting by a user of the system with analog temperature sensor (LM 35) and light dependent resistor (LDR). The system contains LM 35, LDR, Ardino Mega 2560 microcontroller and liquid crystal display (LCD). It can support ON/OFF control of the fan system and the lighting system for smart home and its environment.
On Buffered Double Autoregressive Time Series Models

A buffered double autoregressive (BDAR) time series model is proposed in this paper to depict the buffering phenomenon of conditional mean and conditional variance in time series. To build this model, a novel flexible regime switch mechanism is introduced to modify the classical threshold AR model by capturing the stickiness of signal. Besides, considering the inadequacy of traditional models under the lack of information, a signal retrospection is run in this model to provide a more accurate judgement. Moreover, formal proofs suggest strict stationarity and geometric ergodicity of BDAR model under several sufficient conditions. A Gaussian quasi-maximum likelihood estimation (QMLE) is employed to compute the estimators in this model. The strict proof reveals the asymptotic normality of the estimated coefficients of double AR models. More importantly, it has been demonstrated that the estimated threshold of the BDAR model weakly converges to a functional of a two-sided compound Poisson process. Furthermore, a model selection criteria and asymptotic property have been established. Simulation studies are constructed to evaluate the finite sample performance of QMLE and model selection criteria. Finally, an empirical analysis of Hang Seng Index (HSI) using BDAR model reveals the asymmetry of investors’ preference over losses and gains as well as the asymmetry of volatility structure.
Protokoll zur Viromanalyse bei Infektionen unklarer Genese

Next generation sequencing (NGS) opened a multitude number of new fields of research in medicine and biology. The application of NGS technologies, such as metagenomics, ranges from basic to applied research today. It is generally assumed that NGS will proceed to become a favorite tool in clinical routine diagnostics. Metagenomics describes the nucleic acid-based analysis of entire ecosystems in selected biotopes or samples, enabling the identification of all types of i.e. bacteria or viruses in samples without prior knowledge of the targets. Metagenomic sequencing is increasingly applied to investigate infectious diseases, disease outbreaks or newly emerging diseases. While the technique of sequencing itself is well established, numerous further steps are essential for successful diagnostic of pathogens in clinical samples, especially sample preparation. In this study, an NGS-based virus detection system from non-invasive to minimally invasive clinical samples in case of suspected infections was developed. Therefore, six model viruses (families of Reo-, Pox-, Paramyxo-, Adeno-, Flaviund Bunyaviridae) with different genomes (RNA/DNA) and genome size were selected for the establishment and spiked into human plasma. The reference material was used to combine, modify and optimize various purification techniques and protocols, which were subsequently optimized and modified, compared and combined for the best result. The final protocols (FLUVID: fluid-based unbiased virus detection for viral metagenomics) were verified using the reference material and a representative HIV(human immunodeficiency virus) positive clinical sample. Nucleic acid was analyzed by real-time PCR and NGS on their capability aptitude to detect viruses by viral metagenome. Subsequently, using the established protocols a possible common or individual viral infectious cause in patients with diagnosed FUO (n=80) should be identified. Most common group of FUO was that of infectious diseases caused by viruses, bacteria or fungi. The most prevalent FUOs could be associated with infectious diseases such as HIV (n=12), hepatitis B virus (n=7), tuberculosis (n=6) or infections like malaria (n=2). After unbiased metagenomic sequencing of undiagnosed FUO patients individual viral pathogens could be identified in this work by NGS, including HIV-1, GB virus C, hepatitis E virus, human herpesvirus, Merkel cell polyomavirus, torque teno virus und alphatorque teno virus. The likelihood of identifying viruses in metagenomic studies can be increased by applying the developed FLUVID protocols. In addition, the transferability of the FLUVID protocols to clinical samples has been demonstrated, why they can also be used in clinical diagnostics in the future.
SmartInspect: Smart Contract Inspection Technical Report

Smart contracts are embedded procedures stored with the data they act upon. Debugging deployed Smart Contracts is a difficult task since once deployed, the code cannot be reexecuted and inspecting a simple attribute is not easily possible because data is encoded. In this technical report, we present SmartInspect to address the lack of inspectability of a deployed contract. Our solution analyses the contract state by using decompilation techniques and a mirror-based architecture to represent the object responsible for interpreting the contract state. SmartInspect allows developers and also end-users of a contract to better visualize and understand the contract stored state without needing to redeploy, nor develop any ad-hoc code.
Women in Leadership Positions: Perspectives from an International Consulting Company

In the last decades, academics, researchers and private and public organizations have demonstrated an increasing interest towards women in leadership roles. Without claiming to exhaust this interesting and current topic, this paper discusses the concept of leadership involved in multinational corporations from the perspective of gender equality in light of the current development of female leadership programs that have become the norm in all socially responsible businesses. The research is based on a case study of the effects of such a leadership development program implemented in an international accounting and consulting corporation with a subsidiary in Romania. Through a quantitative analysis based on a web survey, the paper analyses if the women in the corporation report having faced any particular barriers in their pursuit of management positions and if there are differences in the way in which female and male leadership styles are perceived. The main contributions of the paper are an assessment of the effectiveness of the selected leadership development program and a list of recommendations that can be followed by companies with similar leadership development programs in order to support women empowerment and advancement in the company.
Comparison of Various Clinical Methods of Birth Weight Estimation in Term Pregnancy

Background: Knowledge of fetal weight in utero is vital for the obstetrician in deciding whether or not to deliver the fetus as well as in fixing the mode of delivery. Both low birth weight and excessive fetal weight at delivery are associated with increased risk of newborn complications during labor and the puerperium. Various clinical formulae like Johnson’s formula & Dare’s formula are in use for fetal weight estimation. Objectives: The aim of this study was to assess the fetal weight in term pregnancies by various clinical methodsDare’s formula & Johnson’s formula and to compare the methods after knowing the actual weight of the baby after birth. Study Design: It is a prospective observational study of 227 women at term pregnancy at GMERS medical college & Hospital, sola, Ahmadabad from April 2014 to April 2016. The formulas used in this study are: Johnson’s formula and Dare’s formula. The measurements were compared with actual birth weight after the birth of baby. Results: Results vary in terms of accuracy with various methods employed for estimating the fetal weight. This study showed that Dare’s formula was the best method for fetal birth weight estimation. Conclusion: Dare’s formula is an inexpensive method for fetal birth weight estimation. It continues to be used in many countries on large scale because of its low cost, ease of use, and need for little training as the setup for ultra sonographic evaluation is not readily available in rural setups.
How do SME networks evolve? : investigating network context, features and outcomes amongst agrifood SMEs in Greece

This thesis aims to examine the dynamics and interplay between contextual factors and network features and their role in SME network evolution, with a view to building a theory of how context and features affect SME network performance outcomes. Given the complexity of the phenomenon under investigation, a case study methodology was employed. Specifically, four cases of Greek agrifood SME networks were selected of which two exhibited strong performance and two relatively weak performance. Following literature review, the influence of three categories of contextual factor {market conditions, social cohesiveness, external institutional support), and three types of network feature {member profile, competencies, network governance) were examined empirically via the case studies. The research found that all six had some individual influence on performance, but beyond this, certain patterns of interplay between factors could be distinguished. In particular, certain positive . factors were found to counteract the negative influence of other existing (or lacking) factors. Specifically, in terms of network features, governance structure was found to have a countervailing effect over negative performance outcomes produced by diverse member profile and lack of competencies. In addition, the competencies of the constructor of the governance structure were also important in overcoming deficiencies in other features. In terms of network context, social cohesiveness seemed to have a countervailing effect over contextual disincentives for network strengthening, such as unfavourable market conditions or lack of external institutional support. Overall, the research finds that network features are key to explaining the variability in performance displayed by SME networks operating in a very similar context, since they moderate the impacts of external forces on the evolution and performance outcomes of those networks. The research concludes with a proposed framework to explain how context, features and outcomes interact in SME networks.
Facial Skin Smoothening by Iterative Steering Kernel Regression Technique

: The paper enhanced the facial skin using iterative steering kernel regression which is based on edge preserving energy minimization. The region-aware mask allows the user to beautify faces with three major skins attributes homogeneity, lighting, and color. The main aim of this paper is to beautify the skin by decomposition into the three layers using edge preserving filter. After layer decomposition a facial landmarks and significant features extracted from the database to generate the mask into three different layers which allow the user to perform facial beautification simply by adjusting its skin parameter. The combination of these parameters can be optimized automatically it depend on the data base or knowledge base. The experiment result shows that the image is enhanced more as compare to the original image. After three region-aware masks have been obtained, a user can perform facial beautification simply by adjusting the skin parameters. Result of proposed method will be compared with the existing methods in terms of Peak Signal to noise ratio (PSNR) and Mean Square Error (MSE).