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Stop Following Me! Evaluating the Effectiveness of Anti-Stalking Features of Personal Item Tracking Devices
Personal item tracking devices are popular for locating lost items such as keys, wallets, and suitcases. Originally created to help users find personal items quickly, these devices are now being abused by stalkers and domestic abusers to track their victims' location over time. Some device manufacturers created `anti-stalking features' in response, and later improved on them after criticism that they were insufficient. We analyse the effectiveness of the anti-stalking features with five brands of tracking devices through a gamified naturalistic quasi-experiment in collaboration with the Assassins' Guild student society. Despite participants knowing they might be tracked, and being incentivised to detect and remove the tracker, the anti-stalking features were not useful and were rarely used. We also identify additional issues with feature availability, usability, and effectiveness. These failures combined imply a need to greatly improve the presence of anti-stalking features to prevent trackers being abused.
Data-Intensive Supercomputing in the Cloud: Global Analytics for Satellite Imagery
We present our experiences using cloud computing to support data-intensive analytics on satellite imagery for commercial applications. Drawing from our background in high-performance computing, we draw parallels between the early days of clustered computing systems and the current state of cloud computing and its potential to disrupt the HPC market. Using our own virtual file system layer on top of cloud remote object storage, we demonstrate aggregate read bandwidth of 230 gigabytes per second using 512 Google Compute Engine (GCE) nodes accessing a USA multi-region standard storage bucket. This figure is comparable to the best HPC storage systems in existence. We also present several of our application results, including the identification of field boundaries in Ukraine, and the generation of a global cloud-free base layer from Landsat imagery.
Bilgisayar Muhendisligi Egitiminde Teknoloji Egilimlerinin Takip Edilmesi
Today computers have become an integral part of life. However, most people's interaction with computers in on end-user-level. Computer engineers are needed while designing and developing a structure of computer systems, software and hardware systems and also they need when implementing and solving problems while using these systems. Training of qualified computer engineers is vital to have a say in future technology. Recently, big data analysis, cloud technologies, wearable technologies, mobile and online services become popular. For that reason, computer engineering education should update itself regularly and keep up with the latest improvements. In this study, it is touched on some topics which are suggested to extend computer engineering curricula such as big data analyses, wearable technologies internet of things, cloud technologies, identity management and cyber security which are expected to widening in the area and also demanded that computer engineering student should be qualified on. Related topics will be described and usage areas will be explained, developments and future roles will be mentioned and also expected achievements will be described. These achievement's …
Triboelectrically mediated self-assembly and manipulation of drops at an interface
The fluid-fluid interface is a complex environment for a floating object where the statics and dynamics may be governed by capillarity, gravity, inertia, and other external body forces. Yet, the alignment of these forces in intricate ways might result in beautiful pattern formation and self-assembly of these objects, as in the case of bubble rafts or colloidal particles. While interfacial self-assembly has been explored widely, controlled manipulation of floating objects, e.g. drops, at the fluid-fluid interface still remains a challenge largely unexplored. In this work, we reveal the self-assembly and manipulation of water drops floating at an oil-air interface. We show that the assembly occurs due to electrostatic interactions between the drops and their environment. We highlight the role of the boundary surrounding the system by showing that even drops with a net zero electric charge can self-assemble under certain conditions. Using experiments and theory, we show that the depth of the oil bath plays an important role in setting the distance between the self-assembled drops. Furthermore, we demonstrate ways to manipulate the drops actively and passively at the interface.
Effects of Varying the Three-Body Molecular Hydrogen Formation Rate in Primordial Star Formation
The transformation of atomic hydrogen to molecular hydrogen through three-body reactions is a crucial stage in the collapse of primordial, metal-free halos, where the first generation of stars (Population III stars) in the Universe are formed. However, in the published literature, the rate coefficient for this reaction is uncertain by nearly an order of magnitude. We report on the results of both adaptive mesh refinement (AMR) and smoothed particle hydrodynamics (SPH) simulations of the collapse of metal-free halos as a function of the value of this rate coefficient. For each simulation method, we have simulated a single halo three times, using three different values of the rate coefficient. We find that while variation between halo realizations may be greater than that caused by the three-body rate coefficient being used, both the accretion physics onto Population III protostars as well as the long-term stability of the disk and any potential fragmentation may depend strongly on this rate coefficient.
A Multi-Code Analysis Toolkit for Astrophysical Simulation Data
The analysis of complex multiphysics astrophysical simulations presents a unique and rapidly growing set of challenges: reproducibility, parallelization, and vast increases in data size and complexity chief among them. In order to meet these challenges, and in order to open up new avenues for collaboration between users of multiple simulation platforms, we present yt (available at http://yt.enzotools.org/), an open source, community-developed astrophysical analysis and visualization toolkit. Analysis and visualization with yt are oriented around physically relevant quantities rather than quantities native to astrophysical simulation codes. While originally designed for handling Enzo's structure adaptive mesh refinement (AMR) data, yt has been extended to work with several different simulation methods and simulation codes including Orion, RAMSES, and FLASH. We report on its methods for reading, handling, and visualizing data, including projections, multivariate volume rendering, multi-dimensional histograms, halo finding, light cone generation and topologically-connected isocontour identification. Furthermore, we discuss the underlying algorithms yt uses for processing and …
libyt: an In Situ Interface Connecting Simulations with yt, Python, and Jupyter Workflows
In the exascale computing era, handling and analyzing massive datasets have become extremely challenging. In situ analysis, which processes data during simulation runtime and bypasses costly intermediate disk input and output steps, offers a promising solution. We present libyt (https://github.com/yt-project/libyt), an open-source C library that enables astrophysical simulations to analyze and visualize data in parallel computation with yt or other Python packages. libyt can invoke Python routines automatically or provide interactive entry points via a Python prompt or a Jupyter Notebook. It requires minimal intervention in researchers' workflow, allowing users to reuse job submission scripts and Python routines. We describe libyt's architecture for parallel computing in high-performance computing environments, including its bidirectional connection between simulation codes and Python, and its integration into the Jupyter ecosystem. We detail its methods for reading patch-based adaptive mesh refinement (AMR) simulations and handling in-memory data with minimal overhead, and procedures for yielding data when requested by Python. We describe how libyt maps simulation data to yt …
JointNET: A Deep Model for Predicting Active Sacroiliitis from Sacroiliac Joint Radiography
Purpose: To develop a deep learning model that predicts active inflammation from sacroiliac joint radiographs and to compare the success with radiologists. Materials and Methods: A total of 1,537 (augmented 1752) grade 0 SIJs of 768 patients were retrospectively analyzed. Gold-standard MRI exams showed active inflammation in 330 joints according to ASAS criteria. A convolutional neural network model (JointNET) was developed to detect MRI-based active inflammation labels solely based on radiographs. Two radiologists blindly evaluated the radiographs for comparison. Python, PyTorch, and SPSS were used for analyses. P
unyt: Handle, manipulate, and convert data with units in Python
Software that processes real-world data or that models a physical system must have some way of managing units. While simple approaches like the understood convention that all data are in a unit system (such as the MKS SI unit system) do work in practice, they are fraught with possible sources of error both by developers and users of the software. In this paper we present unyt, a Python library based on NumPy and SymPy for handling data that has units. It is designed both to aid quick interactive calculations and to be tightly integrated into a larger Python application or library. We compare unyt with two other Python libraries for handling units, Pint and astropy.units, and find that unyt is faster, has higher test coverage, and has fewer lines of code.
Second Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE2): Submission, Peer-Review and Sorting Process, and Results
This technical report discusses the submission and peer-review process used by the Second Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE2) and the results of that process. It is intended to record both the alternative submission and program organization model used by WSSSPE2 as well as the papers associated with the workshop that resulted from that process.
Dentastik Klinik Yönetimi
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