Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey on the compatibility of Face Detection and Recognition Algorithms after a Pandemic

– Face recognition has attracted many researchers knowing the vast amount of services it can provide. Many computer vision algorithms have been worked upon for extracting unique features from facial images to help differentiate between those unique 2D and 3D(among the most recent once) image representations. The research work ranges from hardcoded elements to modern machine learning methodology and deep learning-based feature engineering and extraction, due to the pandemic caused by covid-19 and safety reasons its almost obvious to see any concerned subject crossing by wearing a mask, which hugely affects the performance of such facial feature extracting models. This survey provides a review of different types of strategies used by various state-of-the-arts and the effects of the changing appearance of subjects in the pandemic and post-pandemic era on pre-pandemic algorithms. First, we summarize the algorithms based on the type of strategies used and describe their importance in performance change caused due to the changing appearance of faces in public places. Second, generalize the core problem in changing results for face detection and recognition algorithms which can be categorized into classes.
This paper is included in the Proceedings of the 31st USENIX Security Symposium

Web Cache Deception (WCD) tricks a web cache into erroneously storing sensitive content, thereby making it widely accessible on the Internet. In a USENIX Security 2020 paper titled “Cached and Confused: Web Cache Deception in the Wild ”, researchers presented the ﬁrst systematic exploration of the attack over 340 websites. This state-of-the-art approach for WCD detection injects markers into websites and checks for leaks into caches. However, this scheme has two fundamental limitations: 1) It cannot probe websites that do not present avenues for marker injection or reﬂection. 2) Marker setup is a burdensome process, making large-scale measurements infeasible. More generally, all previous literature on WCD focuses solely on personal information leaks on web-sites protected behind authentication gates, leaving important gaps in our understanding of the full ramiﬁcations of WCD. We expand our knowledge of WCD attacks, their spread, and implications. We propose a novel WCD detection methodology that forgoes testing prerequisites, and utilizes page iden-ticality checks and cache header heuristics to test any website. We conduct a comparative experiment on 404 websites, and show that our scheme identiﬁes over 100 vulnerabilities while “Cached and Confused” is capped at 18. Equipped with a technique unhindered by the limitations of the previous work, we conduct the largest WCD experiment to date on the Alexa Top 10K, and detect 1188 vulnerable websites. We present case studies showing that WCD has consequences well beyond personal information leaks, and that attacks targeting non-authenticated pages are highly damaging.
Charge-Steering Latches

Modern wireline transceivers are explored to understand the limitations in their design and how their performance in terms of speed and power consumption can be pushed. This exploration leads to the revelation that charge-steering based transceivers have a high potential. As such, an in-depth transient analysis of the basic charge-steering latch is presented, providing new insight into the operation of chargesteering circuits. This insight is utilized to develop design equations for the basic charge-steering latch. The design equations lead to improvement in charge-steering latch design (lower power consumption, higher swing) when compared to prior art. While prior art focused on charge-steering systems (ie. data converters and SerDes), the focus on individual latch improvement could also improve these systems. Several new charge-steering latch topologies are also proposed. The transient analysis is extended to one of these latches, the charge-steering latch with a clocked cross-coupled pair. New design equations are also presented for this latch. Post-layout simulations of these latches are done in 28nm FD-SOI CMOS and further simulations are performed in 65nm CMOS. The new design equations and latch topologies enable the use of charge-steering latches at 28Gb/s and beyond, at new levels of performance. Power savings of as much as 40% are demonstrated by the new topologies, all other specifications being equal. As data transmission speeds are pushed to as much as 1 terabit per second, single lane transceivers are required which operate at 56Gb/s and beyond. The new charge-steering circuits and design equations can enable a 56Gb/s transceiver when used in a half-rate architecture.
A Survey On Depth Estimation Using Vanishing Point

: In the field of computer vision, depth estimation plays an important role. Estimating depth from 2D images is a crucial step in scene reconstruction, 3D object recognition, segmentation, and detection. Depth estimation is often described as an ill posed and inherently ambiguous problem. In this paper, convolutional neural network (CNN) model is used to estimate the depth in a given 2D image using the concept of vanishing point by reviewing papers that attempt to solve the depth estimation problem with various methods including structure from motion, stereo vision, camera calibration, depth from focus and also including the convolutional neural network. Additionally, CNN because of its 3 layers embedded outperforms the other methods and detects the important features without human supervision. We then compare these papers and understand the improvements made over one another. Finally, we explore potential improvements that can aid to better solve this problem.
Automated Diabetic Retinopathy Detection Using Pretrained Deep Neural Network

: : Diabetic retinopathy damages the retina of then patient. It is most frequent in the patients who have had diabetes for longer than 10 years. This problem is occurring in millions of people worldwide but medical practitioners and the tools required for detection of diabetic retinopathy is scare for serving the mass population. The work already was to serve this problem using the application of machine learning but the efficiency of machine learning algorithm depends on the quality of feature extraction which requires domain knowledge. The work presented in this paper overcome the problem by using deep learning algorithm which automatically identifies the pattern and classifies the retina images into one of the five class based .
Airway Epithelial Surfaces Neutrophil Capture and Killing Bacteria on Dehydrated Airway Mucus Prevents Reduced Three-Dimensional Motility in

Cystic ﬁbrosis (CF) lung disease is characterized by persistent lung infection. Thickened (concentrated) mucus in the CF lung impairs airway mucus clearance, which initiates bacterial infection. However, airways have other mechanisms to prevent bacterial infection, including neutrophil-mediated killing. Therefore, we examined whether neutrophil motility and bacterial capture and killing functions are impaired in thickened mucus. Mucus of three concentrations, representative of the range of normal (1.5 and 2.5% dry weight) and CF-like thickened (6.5%) mucus, was obtained from well-differentiated human bronchial epithelial cultures and prepared for three-dimensional studies of neutrophil migration. Neutrophil chemotaxis in the direction of gravity was optimal in 1.5% mucus, whereas 2.5% mucus best supported neutrophil chemotaxis against gravity. Lateral chemokinetic movement was fastest on airway epithelial surfaces covered with 1.5% mucus. In contrast, neutrophils exhibited little motility in any direction in thickened (6.5%) mucus. In in vivo models of airway mucus plugs, neutrophil migration was inhibited by thickened mucus (CF model) but not by normal concentrations of mucus (“normal” model). Paralleling the decreased neutrophil motility in thickened mucus, bacterial capture and killing capacity were decreased in CF-like thickened mucus. Similar results with each mucus concentration were obtained with mucus from CF cultures, indicating that inhibition of neutrophil functions was mucus concentration dependent not CF source dependent. We conclude that concentrated (“thick”) mucus inhibits neutrophil migration and killing and is a key component in the failure of defense against chronic (concen-trated) mucus that characterizes the CF airway. First, we examined two basic features of neutrophil motility in mucus of varying concentrations and containing a chemoattractant, i.e., chemotaxis and chemokinesis. The conventional devices to measure neutrophil migration in gels have measured migration in either the downward (with gravity) or lateral directions (10–13). However, because 50% of airway surfaces face the “upward direction” (antigravita-tional) in vivo, we designed assays to measure both upward and downward neutrophil migration through mucus. These in vitro studies were complemented by in vivo studies in xenografts. Sec-ond, we measured the capacity of neutrophils to capture and kill bacteria in normal and thickened (concentrated) mucus using heat-killed and live bacteria. Finally, to mimic the in vivo situation, studies of neutrophil-bacteria interactions over a range of mucus concentrations were performed with both ciliated and nonciliated human airway epithelial cultures. For all studies, mucus was obtained from human airway epithelial cultures and processed to generate mucus concentrations that spanned normal and CF values. epiﬂuorescence confocal micro- scopes (x-z of neutrophil-associated with epiﬂuorescent x-z confocal scanning, ep- with (cid:1) M calcein-AM. neutrophils 50 (cid:1) of
Cesarean delivery: Preoperative planning and patient preparation

Cesarean delivery: Preoperative planning and patient preparation Author: Vincenzo Berghella, MD Section Editor: Charles J Lockwood, MD, MHCM Deputy Editor: Vanessa A Barss, MD, FACOG All topics are updated as new evidence becomes available and our peer review process is complete. Literature review current through: May 2018. | This topic last updated: Jun 01, 2018. INTRODUCTION — Cesarean delivery (also called cesarean section and cesarean birth) is often cited as the most common major surgical procedure performed in an operating room in the United States [1]. Over 1.2 million cesarean deliveries are performed yearly in the US and comprise almost one-third of births [2]. This topic will review preoperative planning and patient preparation for cesarean delivery. Surgical technique, postoperative issues, repeat cesarean delivery, cesarean delivery on maternal request, and trial of labor after cesarean delivery are discussed separately:
The Value of Political Connections in Fascist Italy-Stock Market Returns and Corporate Networks∗

Recent years have witnessed the flourishing of a body of economic literature concerned with the search for empirical evidence of a positive relation between political connections, economic rent and the value of firms. As a brief survey of some of these works will show, we find in this literature a wide range of definitions of “political connectedness” – from (broadly understood) personal relationships between firms’ owners or the members of its boards and politicians, through financial support offered by firms to electoral campaigns, to the political positions held (now or in the past) by directors of the boards. Johnson and Mitton (2003) present empirical evidence that, in September 1998, Malaysian firms with strong personal ties to the Prime Minister Mahathir benefited from the imposing of capital controls. Khwaja and Mian (2005), by contrast, classify a Pakistani firm as “political connected” if its director participates in an election, and offer accordingly a quantitative estimation of the rent costs of politically connected firms in banking for the Pakistani economy in the years between 1996 and 2002. Faccio et al. (2006), however, do not include contributions to political campaigns or direct (undisclosed) payments to politicians in the definition of political connectedness that they employ in their examination of the link between political connections and corporate bailouts in 35 countries over the period 1997 through 2002. In their study, a company is defined as politically connected if at least one of its top officers or a large shareholder (that is, controlling at least 10% of the company’s voting shares) was head of state, a government minister, or a member of the national parliament, as of the beginning of 1997. Moreover, they take into consideration forms of indirect connection, such as family ties between a head of state or minister and a top officer or a large shareholder, or the well-known “friendship” of a top executive or a large shareholder with a head of state, government minister, or member of parliament. In analysing the development of China’s private sector, Li et al. (2008) define political connection on the basis of the personal affiliation of the owners of private firms with the
Real Time Object Detection using Deep Learning: A Webcam Based Approach

: Object Detection has come to the forefront as one of the most important applications of deep learning which is characterized by its learning ability of features and depiction of features compared to the traditional object detection methods. Region proposal algorithms form the basis of which Object Detection networks hypothesize object regions. Algorithms that have cut down on the running time of these detection networks are SPPnet and Fast R-CNN. In this paper we propose a method for object detection through webcam. The method used is a combination of Region Proposal Network (RPN) and Fast R-CNN, where high quality region proposals are generated by training the RPN end to end, which are in turn is used by Fast-RCNN for detection. These two modules combine to generate an object detection system called Faster R-CNN. Deep models can automatically act as a classifier and detection device, they do not require hand engineered technologies. Therefore, one of the greatest prospects of deep learning technology is in Object Detection. One of the major focus in computer vision technologies is object detection, which has been applied in the pedestrian detection driverless car, video surveillance robotics and counting techniques. The Faster R-CNN models: InceptionV2 and ResNet50 have been used for object detection using webcam, i.e. real time object detection.
Programmable active pixel sensor to investigate neural interactions within the retina

Detection of the visual scene by the eye and the resultant neural interactions of the retina-brain system give us our perception of sight. We have developed an Active Pixel Sensor (APS) to be used as a tool for both furthering understanding of these interactions via experimentation with the retina and to make developments towards a realisable retinal prosthesis. The sensor consists of 469 pixels in a hexagonal array. The pixels are interconnected by a programmable neural network to mimic lateral interactions between retinal cells. Outputs from the sensor are in the form of biphasic current pulse trains suitable to stimulate retinal cells via a biocompatible array. The APS will be described with initial characterisation and test results.
Modeling Behavioral Aspects of Urban Freight Movement in Supply Chains

Decision makers in freight transportation need to assess new distribution systems and the impacts of changes in the freight distribution environment on infrastructure needs and usage, logistical performance, emissions, and energy use. There is a need, therefore, for behavioral models that can predict goods flows and vehicle flows in both current and future situations. This research outlines a conceptual framework consisting of the markets, actors, and supply chain elements of freight movement. Supply chains are constructed by linking distribution channels (of different logistics characteristics) between different activity types, such as consumers, supermarkets, stores, offices, distribution centers, and factories. The framework outlined in this research was used to develop the GoodTrip model—a demand-driven, commodity-based freight movement model that incorporates supply chains. Starting with consumer demand, the model estimates goods flows and simulates vehicle tours. The open architecture of the model allows mixed use of empirical data, behavioral models, and scenario-type assumptions. The behavioral models will be developed in future research. In its first application, the GoodTrip model was used to compare the logistical performance and external impacts of three types of urban distribution systems: the traditional system and two concepts using urban distribution centers (one using vans, the other using automated underground vehicles). The results show considerable differences in the performance and effects of the alternatives, especially when they are applied to different types of distribution channels, such as food retail stores or bookstores.
Electrophotographic Direct-Write for Electronics

Electrophotographic direct-write is a technique that has been developed to directly pattern dry powder images to a substrate. These powder images can then be fused to create the components and interconnects of an electronic circuit. Concepts of electrophotography were used to envision a new direct-write system capable of printing conductive and resistive powders. This system was developed into a test bed to conduct investigations on the feasibility of this technique. Initial experimentation was conducted to prove the system could work with both conductive (nickel) and non-conductive (oxidized copper) dry powders. With the successful patterning of these powders, testing was done to improve the quality of the printed image and increase the printed powder density.
Multiple fault diagnosis in graph-based systems

Graph-based systems are models wherein the nodes represent the components and the edges represent the fault propagation between the components. For critical systems, some components are equipped with smart sensors for on-board system health management. When an abnormal situation occurs, alarms will be triggered from these sensors. This paper considers the problem of identifying the set of potential failure sources from the set of ringing alarms in graph-based systems. However, the computational complexity of solving the optimal multiple fault diagnosis problem is super-exponential. Based on Lagrangian relaxation and subgradient optimization, we present a heuristic algorithm to find the most likely candidate fault set. A computationally cheaper heuristic algorithm - primal heuristic - has also been applied to the problem so that real-time multiple fault diagnosis in systems with several thousand failure sources becomes feasible in a fraction of a second.
Developments at DWD: Integrated water vapour (IWV) from ground-based GPS

The delay of radio signals from several GPS satellites to a GPS receiver at the ground can be processed to derive the integrated water vapour (IWV) in the atmospheric column above the station with good accuracy, provided that the pressure and temperature at the GPS station are also known (Bevis et al., 1994). In 2001 and 2002, DWD has tested the assimilation of hourly or half-hourly data from some 100 GPS Stations in Germany processed by the GeoForschungsZentrum Potsdam (GFZ, see Reigber et al., 2002; Gendt et al., 2004) into the then operational configuration of the COSMO model (formerly Lokal Modell LM) which had a mesh width of 7 km (Tomassini, 2003; Tomassini and Schraff, 2002).
Exponentially convergent estimator to improve performance of voltage source converters

In this study, the authors introduce an exponentially convergent estimator of load power to improve the performance of voltage source converter (VSC) applications. This estimator, based on a non-linear reduced-order observer, is used to estimate the load power and its time derivative. The estimated signals are feedforwarded to build a non-linear controller in order to improve the whole system performance. The main advantage of the proposed estimator is that it presents linear error dynamics which allows to design the observer by using linear techniques. In this way, the observer transient performance (i.e. convergence rate) is clearly established in the whole VSC operation range. The feedback controller is based on input-output feedback linearisation considering the energy as output so that the DC-voltage internal dynamics is avoided and linear control strategies can be also used in the controller design. Several tests - including performance in presence of switching frequency, parameter uncertainties and disturbances in the AC input voltage - validate the proposed technique.
Integration of smartphone technology for maize recognition

The study of android based maize assessment was done by involving two popular machine learning software i.e. teachable machine and android studio. The classification model was performed in online teachable machine learning while interface generation was performed in android studio. Various maize tassel from male, female and contamination plants were collected and used for training and model validation. The results indicated that Android-based tassel classification was successfully applied to the study area with accuracy of 80.7%. In addition, the error of classification was 19.3%, a relatively lower values for large testing datasets. Several mis-classification were found particularly at similar tassel shape. The integration of the model with smartphone technology enables rapid recognition of off-type plant at real-time, even though operated by personnel with limited skills or no knowledge seed technology on maize parental lines ideotype.
Technological assurance of reliability of centrifugal pumps for pumping biomass processing products based on adaptive dampers

In almost every mechanical system, moving mechanisms slide over stationary parts, creating friction and, as a result, unwanted energy losses. In engineering, sliding or rolling bearings are most often used as supports. However, any system can benefit from a greater reduction in friction between components. As will be shown in this article, the stability problem can be solved by blocking vibrations in the radial direction. The latest technological advances in the field of manufacturing magnetic materials make it possible to integrate magnetic bearings with permanent magnetization (hereinafter - MBPM) into a larger number of mechanical systems. This blocking of radial movement is carried out without the use of a mechanical sliding bearing, chosen for its simplicity and ease of integration. To facilitate the integration of the MBPM into the overall system of the device, it is important to know the mechanical properties of magnetic bearings, namely stiffness and damping, as well as the performance characteristics and limits of their operation. This article examines the possibility of using an adaptive damper in centrifugal pumps to ensure the technological reliability of the equipment. Alternating permanent magnets in the direction of their movement is the most optimal option, leading to large and smooth hysteresis loops of force - displacement. The proposed arrangement of magnets ensures the adaptability of the device with the determination of its optimal size, and also takes into account the edge and surface effects in the design of the damper. In addition, the article discusses theoretical and technical issues of levitation - free floating of bodies. Magnetic suspension can be used to study only those processes where mechanical connections are undesirable. The use of magnetic suspension for balancing centrifugal pumps during transportation of biomass processing products, supports of mixing devices in reactors in biomass processing reactors and other machine components opens up wide opportunities.