Miracle-dz/UniFAR-FTUs huggingface dataset:
Defeating Denial-of-Service Attacks in a Self-Managing N-Variant System

N-variant systems protect software from attack by executing multiple variants of a single program in parallel, checking regularly that they are behaving consistently. The variants are designed to behave identically during normal operation and differently during an attack. When different behavior (divergence) is detected, N-variant systems self-heal by either rolling back to a safe state or restarting. Unfortunately, an attacker can create a denial-of-service (DoS) attack from a diverging input by using it to force an N-variant system into an endless diverge/restart cycle. This paper describes a defense, CRISPR-Inspired Program Resiliency (Crispy), that automatically protects N-variant systems from such DoS attacks. Crispy mitigates DoS attacks against N-variant systems using an automatic signature generation technique modeled on CRISPR/Cas, the bacterial adaptive immune system. Experiments on two webservers using exploits developed by an independent Red Team showed Crispy protected against 87.5% of DoS attacks with zero false positives. Overhead was minimal and varied according to the number of signatures maintained, which can be tailored to the threat model and performance requirements.
The Integration and Establishment of a Wedding Reception Information System Using Service-oriented Architecture

In a dynamic market environment, business owners are facing market saturation, accelerated elimination of products through competition, and a modern society that emphasizes differentiation. To avoid losing advantages and competitive capacity, businesses must provide diversified and combined services to achieve the synergistic effect of complementary services. A majority of wedding receptions in Taiwan are held as open-air banquets or in restaurants/hotels. Caterers design and decorate venues after verbal discussions with clients, not only becoming a lengthy and costly process, but also making the reception venue somewhat monontonous and unexciting. Menu selection is limited to the standard menu provided by the caterers, resulting in a lack of flexibility to accommodate customized changes. Because invited guests might visit a certain restaurant or hotel only once in their lifetime, great care is required for selecting the venue. If the day of the wedding must fall on an auspicious date, this means that other couples are likely hosting wedding receptions at the same venue on the same day. The venue parking lot may not be spacious enough to accommodate all the vehicles, signifying that the restaurant might have to rent another parking lot. For guests attending the wedding reception, locating the parking lots temporarily rented by the restaurant takes effort and is time consuming. Therefore, this study employed a service-oriented architecture (SOA) to integrate the three main heterogenous service systems developed by this study (the wedding venue decoration system, the banquet menu layout system, and the parking navigation and management system) for the establishment of a wedding reception information system. This system can serve as a digital system for caterers and restaurant business owners when hosting wedding receptions, enhancing their industry value and competitive market capacity in the food and dining industry. Restaurants and caterers can also provide innovative services by using this system, establishing their market position.
THE CRITICAL ROLE OF INTELLIGENT SOFTWARE AGENTS IN ENABLING NET-CENTRIC COMMAND AND CONTROL

Abstract : The Net-Centric Operations and Warfare Reference Model (NCOW RM), Version 1.0, currently under development by the Office of the Assistant Secretary of Defense for Networks and Information Integration (OASD/NII), is the architectural touchstone for all Department of Defense information systems of the near- and long-term future. The reference model prescribes four sets of fundamental functionality: user/entity interaction services; intelligent assistance capabilities; net-centric services (encompassing communities of interest (COI) services, core enterprise services (discovery, mediation, collaboration, etc.), and environment control services); and resource provisioning services. The role and critical importance of the set of services that are to provide intelligent assistance to users of the Global Information Grid (GIG), and to its envisaged Net-centric Information Environment (NCIE), has yet to be fully delineated. We argue that the envisaged net-centricity in future warfare (command and control), business operations, and enterprise management is dependent upon a robust intelligent assistance capability based on the profuse use of intelligent agents throughout the GIG's NCIE.
Improving reliability of mobile manipulators against unknown external faults

A robot (e.g. mobile manipulator) that interacts with its environment to perform its tasks, often faces situations in which it is unable to achieve its goals despite perfect functioning of its sensors and actuators. These situations occur when the behavior of the object(s) manipulated by the robot deviates from its expected course because of unforeseeable ircumstances. These deviations are experienced by the robot as unknown external faults. In this work we present an approach that increases reliability of mobile manipulators against the unknown external faults. This approach focuses on the actions of manipulators which involve releasing of an object. The proposed approach, which is triggered after detection of a fault, is formulated as a three-step scheme that takes a definition of a planning operator and an example simulation as its inputs. The planning operator corresponds to the action that fails because of the fault occurrence, whereas the example simulation shows the desired/expected behavior of the objects for the same action. In its first step, the scheme finds a description of the expected behavior of the objects in terms of logical atoms (i.e. description vocabulary). The description of the simulation is used by the second step to find limits of the parameters of the manipulated object. These parameters are the variables that define the releasing state of the object. Using randomly chosen values of the parameters within these limits, this step creates different examples of the releasing state of the object. Each one of these examples is labelled as desired or undesired according to the behavior exhibited by the object (in the simulation), when the object is released in the state corresponded by the example. The description vocabulary is also used in labeling the examples autonomously. In the third step, an algorithm (i.e. N-Bins) uses the labelled examples to suggest the state for the object in which releasing it avoids the occurrence of unknown external faults. The proposed N-Bins algorithm can also be used for binary classification problems. Therefore, in our experiments with the proposed approach we also test its prediction ability along with the analysis of the results of our approach. The results show that under the circumstances peculiar to our approach, N-Bins algorithm shows reasonable prediction accuracy where other state of the art classification algorithms fail to do so. Thus, N-Bins also extends the ability of a robot to predict the behavior of the object to avoid unknown external faults. In this work we use simulation environment OPENRave that uses physics engine ODE to simulate the dynamics of rigid bodies.
Diagnostik und Management von Gefäßmalformationen Interdisziplinäres Teamwork gefragt Hintergrund

Vascular anomalies are very rare, but can occur in children and adults in almost every region of the body. Due to the complexity of this disease, the path to a definitive diagnosis is often difficult. It requires interdisciplinary teamwork with close exchange of information between the individual treatment partners to reach the correct diagnosis and then to start the best therapy. This article provides an overview of the main types of vascular malformations from a clinical, imaging, and histological point of view, following the current classification of the International Society for the Study of Vascular Anomalies (ISSVA).
Information Retrieval in Wikis - Wie können klassische Methoden des Information Retrievals die Suchfunktion eines Wikis bereichern?

As collective accumulation of knowledge, wikis enjoy increasing popularity. But accumulated knowledge can only be used effectively if search tools are available which allow for the specifics of the environment that has to be searched. However, the results that are found by a wiki's search function are quite often not relevant. This article describes the structure and functionality of an optimized search engine for wikis, which will be able to remove the above mentioned drawback from wiki search functions. To present search results in a ranking, certain criteria have to be fulfilled. However, a lot of web criteria cannot be used for wiki search result rankings. Therefore the wikis' structure has to be analysed precisely. This article will describe how search results can be displayed in clusters that are based on the special structure of wikis.
Intention Detection of Gait Adaptation in Natural Settings

Gait adaptation is an important part of gait analysis and its neuronal origin and dynamics has been studied extensively. In neurorehabilitation, it is important because it enables neuroplasticity mechanisms and facilitates the restoration of motor function. For this reason, brain-computer interfaces (BCI) have been build to facilitate neurorehabilitation. This paper presents a gait adaptation scheme in natural settings. It allows monitoring of subjects in more realistic environment without the requirement of specialized equipment such as treadmill and foot pressure sensors. We extract gait characteristics based on a single RGB camera whereas wireless EEG signals are monitored simultaneously. Based on Regularised Common Spatial Patterns (RCSP) that take into consideration both amplitude and frequency EEG features, we demonstrate that the method can not only successfully detect adaptation steps but it also detect efficiently whether the subject adjust their pace to higher or lower speed.
Modelling the variance of respondent-driven sampling using Reddit

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MagneticSpy: Exploiting Magnetometer in Mobile Devices for Website and Application Fingerprinting

Recent studies have shown that aggregate CPU usage and power consumption traces on smartphones can leak information about applications running on the system or websites visited. In response, access to such data has been blocked for mobile applications starting from Android 8. In this work, we explore a new source of side-channel leakage for this class of attacks. Our method is based on the fact that electromagnetic activity caused by mobile processors leads to noticeable disturbances in magnetic sensor measurements on mobile devices, with the amplitude being proportional to the CPU workload. Therefore, recorded sensor data can be analyzed to reveal information about ongoing activities. The attack works on a number of devices: we evaluated 80 models of modern smartphones and tablets and observed the reaction of the magnetometer to the CPU activity on 56 of them. On selected devices we were able to successfully identify which application has been opened (with up to 90% accuracy) or which web page has been loaded (up to 91% accuracy). The presented side channel poses a significant risk to end users' privacy, as the sensor data can be recorded from native apps or even from web pages without user permissions. Finally, we discuss possible countermeasures to prevent the presented information leakage.
Generic Construction of the Standard Model Gauge Group and Matter Representations in F‐theory

We describe general classes of 6D and 4D F‐theory models with gauge group (SU(3)×SU(2)×U(1))/Z6 . We prove that this set of constructions gives all possible consistent 6D supergravity theories with no tensor multiplets having this gauge group and the corresponding generic matter representations, which include those of the MSSM. We expect, though do not prove, that these models are similarly generic for 6D theories with tensor multiplets and for 4D N=1 supergravity theories. The largest class of these constructions come from deforming an underlying geometry with gauge symmetry SU(4)×SU(3)×SU(2) .
Compact Disks in a High-resolution ALMA Survey of Dust Structures in the Taurus Molecular Cloud

We present a high-resolution (∼0.″12, ∼16 au, mean sensitivity of 50 μJy beam−1 at 225 GHz) snapshot survey of 32 protoplanetary disks around young stars with spectral type earlier than M3 in the Taurus star-forming region using the Atacama Large Millimeter Array. This sample includes most mid-infrared excess members that were not previously imaged at high spatial resolution, excluding close binaries and objects with high extinction, thereby providing a more representative look at disk properties at 1–2 Myr. Our 1.3 mm continuum maps reveal 12 disks with prominent dust gaps and rings, 2 of which are around primary stars in wide binaries, and 20 disks with no resolved features at the observed resolution (hereafter smooth disks), 8 of which are around the primary star in wide binaries. The smooth disks were classified based on their lack of resolved substructures, but their most prominent property is that they are all compact with small effective emission radii (Reff,95% ≲ 50 au). In contrast, all disks with Reff,95% of at least 55 au in our sample show detectable substructures. Nevertheless, their inner emission cores (inside the resolved gaps) have similar peak brightness, power-law profiles, and transition radii to the compact smooth disks, so the primary difference between these two categories is the lack of outer substructures in the latter. These compact disks may lose their outer disk through fast radial drift without dust trapping, or they might be born with small sizes. The compact dust disks, as well as the inner disk cores of extended ring disks, that look smooth at the current resolution will likely show small-scale or low-contrast substructures at higher resolution. The correlation between disk size and disk luminosity correlation demonstrates that some of the compact disks are optically thick at millimeter wavelengths.
Assessing Post Deletion in Sina Weibo: Multi-modal Classification of Hot Topics

Widespread Chinese social media applications such as Weibo are widely known for monitoring and deleting posts to conform to Chinese government requirements. In this paper, we focus on analyzing a dataset of censored and uncensored posts in Weibo. Despite previous work that only considers text content of posts, we take a multi-modal approach that takes into account both text and image content. We categorize this dataset into 14 categories that have the potential to be censored on Weibo, and seek to quantify censorship by topic. Specifically, we investigate how different factors interact to affect censorship. We also investigate how consistently and how quickly different topics are censored. To this end, we have assembled an image dataset with 18,966 images, as well as a text dataset with 994 posts from 14 categories. We then utilized deep learning, CNN localization, and NLP techniques to analyze the target dataset and extract categories, for further analysis to better understand censorship mechanisms in Weibo. We found that sentiment is the only indicator of censorship that is consistent across the variety of topics we identified. Our finding matches with recently leaked logs from Sina Weibo. We also discovered that most categories like those related to anti-government actions (e.g. protest) or categories related to politicians (e.g. Xi Jinping) are often censored, whereas some categories such as crisis-related categories (e.g. rainstorm) are less frequently censored. We also found that censored posts across all categories are deleted in three hours on average.
Stable numerical schemes for nonlinear dispersive equations with counter-propagation and gain dynamics

We develop a stable and efficient numerical scheme for modeling the optical field evolution in a nonlinear dispersive cavity with counter-propagating waves and complex, semiconductor physics gain dynamics that is expensive to evaluate. Our stability analysis is characterized by a von Neumann analysis that shows that many standard numerical schemes are unstable due to competing physical effects in the propagation equations. We show that the combination of a predictor–corrector scheme with an operator splitting not only results in a stable scheme, but also provides a highly efficient, single-stage evaluation of the gain dynamics. Given that the gain dynamics is the rate-limiting step of the algorithm, our method circumvents the numerical instability induced by the other cavity physics when evaluating the gain in an efficient manner. We demonstrate the stability and efficiency of the algorithm on a diode laser model that includes three waveguides and semiconductor gain dynamics. The laser is able to produce a repeating temporal waveform and stable optical comb lines, thus demonstrating that frequency comb generation may be possible in chip-scale, diode lasers.
On the performance of residual block design alternatives in convolutional neural networks for end-to-end audio classification

Residual learning is a recently proposed learning framework to facilitate the training of very deep neural networks. Residual blocks or units are made of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or residual connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers that make up a residual block. While ResNet architectures for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, few works have adopted ResNet architectures so far for 1D audio classification tasks. Thus, the suitability of different residual block designs for raw audio classification is partly unknown. The purpose of this paper is to analyze and discuss the performance of several residual block implementations within a state-of-the-art CNN-based architecture for end-to-end audio classification using raw audio waveforms. For comparison purposes, we analyze as well the performance of the residual blocks under a similar 2D architecture using a conventional time-frequency audio represen-tation as input. The results show that the achieved accuracy is considerably dependent, not only on the specific residual block implementation, but also on the selected input normalization.
2-D Analysis Based ILC for Linear Time-Variant Discrete Systems with Multiple Time Delays

This paper presents Two-Dimensional (2-D) system analysis based Iterative Learning Control (ILC) methods for Linear Time-Variant (LTV) discrete systems with multiple time delays. Both the state delays and the input delays are considered in the ILC design. The ILC research strategy for LTV discrete systems with multiple time delays is to rescontruct the derived ILC error equations into the 2-D Roessor's model, so that the convergence conditions for the proposed ILC rules can be derived according to the convergence property of the 2-D Roessor's model. A numerical example is used to validate the effectiveness of the proposed ILC methods.
Finding Quantum Many-Body Ground States with Artificial Neural Network

Solving ground states of quantum many-body systems has been a long-standing problem in condensed matter physics. Here, we propose a new unsupervised machine learning algorithm to find the ground state of a general quantum many-body system utilizing the benefits of artificial neural network. Without assuming the specific forms of the eigenvectors, this algorithm can find the eigenvectors in an unbiased way with well controlled accuracy. As examples, we apply this algorithm to 1D Ising and Heisenberg models, where the results match very well with exact diagonalization.
EdgeLens: Deep Learning based Object Detection in Integrated IoT, Fog and Cloud Computing Environments

Data-intensive applications are growing at an increasing rate and there is a growing need to solve scalability and high-performance issues in them. By the advent of Cloud computing paradigm, it became possible to harness remote resources to build and deploy these applications. In recent years, new set of applications and services based on Internet of Things (IoT) paradigm, require to process large amount of data in very less time. Among them surveillance and object detection have gained prime importance, but cloud is unable to bring down the network latencies to meet the response time requirements. This problem is solved by Fog computing which harnesses resources in the edge of the network along with remote cloud resources as required. However, there is still a lack of frameworks that are successfully able to integrate sophisticated software and applications, especially deep learning, with fog and cloud computing environments. In this work, we propose a framework to deploy deep learning-based applications in fog-cloud environments to harness edge and cloud resources to provide better service quality for such applications. Our proposed framework, called EdgeLens, adapts to the application or user requirements to provide high accuracy or low latency modes of services. We also tested the performance of the software in terms of accuracy, response time, jitter, network bandwidth and power consumption and show how EdgeLens adapts to different service requirements.
Distributed Optimization for Smart Cyber-Physical Networks

The presence of embedded electronics and communication capabilities as well as sensing and control in smart devices has given rise to the novel concept of cyber-physical networks, in which agents aim at cooperatively solving complex tasks by local computation and communication. Numerous estimation, learning, decision and control tasks in smart networks involve the solution of large-scale, structured optimization problems in which network agents have only a partial knowledge of the whole problem. Distributed optimization aims at designing local computation and communication rules for the network processors allowing them to cooperatively solve the global optimization problem without relying on any central unit. The purpose of this survey is to provide an introduction to distributed optimization methodologies. Principal approaches, namely (primal) consensus-based, duality-based and constraint exchange methods, are formalized. An analysis of the basic schemes is supplied, and state-of-the-art extensions are reviewed.