Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid detection of disobedient forwarding on compromised OpenFlow switches

Software-defined networking (SDN) allows network administrators to manage network flows easily from a centralized controller. However, it also leads to new security threats to applications, controllers, OpenFlow switches, topology management and so on. In this work, we design a method to detect disobedient forwarding in the flow table by compromising a switch. To enhance detection efficiency and minimize additional network traffic, we reduce the number of detection packets necessary by aggregating the flow entries. This method selects the flow entries whose match fields can compose a valid packet from multiple switches. The switches on which the entries are form a path that allows the packet to travel through for rapid detection. We evaluate the efficiency of this detection method for various topology types in typical data center networks by Mininet simulation. The experimental results demonstrate that this method can examine the forwarding correctness of around 3 flow entries simultaneously for each detection packet in fat-tree topology. Furthermore, the scale of the network topology does not affect the efficiency of the method significantly.
Joint geometric graph embedding for partial shape matching in images

A novel multi-criteria optimization framework for matching of partially visible shapes in multiple images using joint geometric graph embedding is proposed. The proposed framework achieves matching of partial shapes in images that exhibit extreme variations in scale, orientation, viewpoint and illumination and also instances of occlusion; conditions which render impractical the use of global contour-based descriptors or local pixel-level features for shape matching. The proposed technique is based on optimization of the embedding distances of geometric features obtained from the eigenspectrum of the joint image graph, coupled with regularization over values of the mean pixel intensity or histogram of oriented gradients. It is shown to obtain successfully the correspondences denoting partial shape similarities as well as correspondences between feature points in the images. A new benchmark dataset is proposed which contains disparate image pairs with extremely challenging variations in viewing conditions when compared to an existing dataset [18]. The proposed technique is shown to significantly outperform several state-of-the-art partial shape matching techniques on both datasets.
Quasinormal modes of AdS black holes and the approach to thermal equilibrium

We investigate the decay of a scalar field outside a Schwarzschild anti de Sitter black hole. This is determined by computing the complex frequencies associated with quasinormal modes. There are qualitative differences from the asymptotically flat case, even in the limit of small black holes. In particular, for a given angular dependence, the decay is always exponential - there are no power law tails at late times. In terms of the AdS/CFT correspondence, a large black hole corresponds to an approximately thermal state in the field theory, and the decay of the scalar field corresponds to the decay of a perturbation of this state. Thus one obtains the timescale for the approach to thermal equilibrium. We compute these timescales for the strongly coupled field theories in three, four, and six dimensions which are dual to string theory in asymptotically AdS spacetimes.
3D vision sensing for improved pedestrian safety

Pedestrian-vehicle accidents account for the second largest source of automotive related fatality and injury worldwide. This paper presents a system which detects and tracks pedestrians in realtime for use with automotive pedestrian protection systems (PPS) aimed at reducing such pedestrian-vehicle related injury. The system is based on a passive stereo vision configuration which segments a scene into 3D objects, classifies each object as pedestrian/non-pedestrian and finally tracks the pedestrian in 3D. Our system was implemented and tested on a Volvo test vehicle. Strong results for the system were obtained over a range of simple and complex environments, with average positive and false positive detection rates of 83.5% and 0.4%, respectively.
Chameleon: a resource scheduler in a data grid environment

Grid computing is moving into two ways. The Computational Grid focuses on reducing execution time of applications that require a great number of computer processing cycles. The Data Grid provides the way to solve large scale data management problems. Data intensive applications such as High Energy Physics and Bioinformatics require both Computational and Data Grid features. Job scheduling in Grid has been mostly discussed from the perspective of computational Grid. However, scheduling on Data Grid is just a recent focus of Grid computing activities. In Data Grid environment, effective scheduling mechanism considering both computational and data storage resources must be provided for large scale data intensive applications. In this paper, we describe new scheduling model that considers both amount of computational resources and data availability in Data Grid environment. We implemented a scheduler, called Chameleon, based on the proposed application scheduling model. Chameleon shows performance improvements in data intensive applications that require both large number of processors and data replication mechanisms. The results achieved from Chameleon are presented.
Semantic Smoothing for Bayesian Text Classification with Small Training Data

Bayesian text classifiers face a common issue which is referred to as data sparsity problem, especially when the size of training data is very small. The frequently used Laplacian smoothing and corpus-based background smoothing are not effective in handling it. Instead, we propose a novel semantic smoothing method to address the sparse problem. Our method extracts explicit topic signatures (e.g. words, multiword phrases, and ontologybased concepts) from a document and then statistically maps them into single-word features. We conduct comprehensive experiments on three testing collections (OHSUMED, LATimes, and 20NG) to compare semantic smoothing with other approaches. When the size of training documents is small, the bayesian classifier with semantic smoothing not only outperforms the classifiers with background smoothing and Laplacian smoothing, but also beats the state-of-the-art active learning classifiers and SVM classifiers. In this paper, we also compare three types of topic signatures with respect to their effectiveness and efficiency for semantic smoothing.
Green Computing: Power Optimisation of VFI-Based Real-Time Multiprocessor Dataflow Applications

Execution time is no longer the only performance metric for computer systems. In fact, a trend is emerging to trade raw performance for energy savings. Techniques like Dynamic Power Management (DPM, switching to low power state) and Dynamic Voltage and Frequency Scaling (DVFS, throttling processor frequency) help modern systems to reduce their power consumption while adhering to performance requirements. To balance flexibility and design complexity, the concept of Voltage and Frequency Islands (VFIs) was recently introduced for power optimisation. It achieves fine-grained system-level power management, by operating all processors in the same VFI at a common frequency/voltage. This paper presents a novel approach to compute a power management strategy combining DPM and DVFS. In our approach, applications (modelled in full synchronous dataflow, SDF) are mapped on heterogeneous multiprocessor platforms (partitioned in voltage and frequency islands). We compute an energy optimal schedule, meeting minimal throughput requirements. We demonstrate that the combination of DPM and DVFS provides an energy reduction beyond considering DVFS or DMP separately. Moreover, we show that by clustering processors in VFIs, DPM can be combined with any granularity of DVFS. Our approach uses model checking, by encoding the optimisation problem as a query over priced timed automata. The model-checker UPPAAL Cora extracts a cost minimal trace, representing a power minimal schedule. We illustrate our approach with several case studies on commercially available hardware.
Indirect Channel Sensing for Cognitive Amplify-and-Forward Relay Networks

In cognitive radio network the primary channel information is beneficial. But it can not be obtained by direct channel estimation in cognitive system as pervious methods. And only one possible way is the primary receiver broadcasts the primary channel information to the cognitive users, but it would require the modification of the primary receiver and additional precious spectrum resource. Cooperative communication is also a promising technique. And this paper introduces an indirect channel sensing method for the primary channel in cognitive amplify-and-forward (AF) relay network. As the signal retransmitted from the primary AF relay node includes channel effects, the cognitive radio can receive retransmitted signal from AF node, and then extract the channel information from them. Afterwards, Least squares channel estimation and sparse channel estimation can be used to address the dense and sparse multipath channels respectively. Numerical experiment demonstrates that the proposed indirect channel sensing method has an acceptable performance.
A System for a Hand Gesture-Manipulated Virtual Reality Environment

Extensive research has been done using machine learning techniques for hand gesture recognition (HGR) using camera-based devices; such as the Leap Motion Controller (LMC). However, limited research has investigated machine learning techniques for HGR in virtual reality applications (VR). This paper reports on the design, implementation, and evaluation of a static HGR system for VR applications using the LMC. The gesture recognition system incorporated a lightweight feature vector of five normalized tip-to-palm distances and a k-nearest neighbour (kNN) classifier. The system was evaluated in terms of response time, accuracy and usability using a case-study VR stellar data visualization application created in the Unreal Engine 4. An average gesture classification time of 0.057ms with an accuracy of 82.5% was achieved on four distinct gestures, which is comparable with previous results from Sign Language recognition systems. This shows the potential of HGR machine learning techniques applied to VR, which were previously applied to non-VR scenarios such as Sign Language recognition.
Reciprocal fairness equilibrium evaluation in the game of the urban housing demolition

Urban housing demolition problem involves multi-interests of government, developers, and relocatees. How to coordinate the interests in the process of urban house demolition is a very important theoretical issue in the process of the national urbanization. On the analytical basis of inequity aversion model, this paper constructs the reciprocal fairness evaluation of government and developers, developers and relocatees, government and relocatees and conducts analysis of reciprocal fairness equilibrium between stakeholders. Finally, selecting Changsha Stubborn Nail as a case, it gives a analysis in-depth in the role of reciprocal fairness idea through using relevant data.
Analysis of notification methods with respect to mobile system characteristics

Recently, there has been an increasing need for secure, efficient and simple notification methods for mobile systems. Such systems are meant to provide users with precise tools best suited for work or leisure environments and a lot of effort has been put into creating a multitude of mobile applications. However, not much research has been put at the same time into determining which of the available protocols are best suited for individual tasks. Here a number of basic notification methods are presented and tests are performed for the most promising ones. An attempt is made to determine which methods have the best throughput, latency, security and other characteristics. A comprehensive comparison is provided, which can be used to select the right method for a specific project. Finally, conclusions are provided and the results of all the tests conducted are discussed.
Robust person authentication using dyadic wavelet filters learned by cosine-maximization

This paper produces a person authentication system using learned lifting dyadic wavelet filters. Our system has the capability of identifying persons whose facial image is changed the size and angle of an original image. The learning algorithm is done by training free parameters in the lifting filters so as to maximize the cosine between a vector whose components are the lifting filters and a vector of facial parts. This problem can be solved fast using Newton’s method. Applying the learned filters to a test image, facial parts in the image can be detected. Our detection of facial parts is robust for expansion, shrink and rotation of the image. A person is identified by checking the number of faces detected from video frames. Simulation results show that our person authentication is accurate and fast.
Optimal energy allocation for wireless communications powered by energy harvesters

We consider the use of energy harvesters, in place of conventional batteries with fixed energy storage, for point-to-point wireless communications. In addition to the challenge of transmitting in a channel with time selective fading, energy harvesters provide a perpetual but unreliable energy source. In this paper, we consider the problem of energy allocation over a finite horizon, taking into account a time varying channel and energy source, so as to maximize the throughput. Two types of side information are assumed to be available: causal side information of the immediate past channel condition and harvested energy, and full side information. We obtain structural results for the optimal energy allocation, via the use of dynamic programming and convex optimization techniques. In particular, if unlimited energy can be stored in the battery with harvested energy, we prove the optimality of a water-filling energy allocation solution with multiple, non-decreasing water levels.
RAPID COOLING OF THE NEUTRON STAR IN THE QUIESCENT SUPER-EDDINGTON TRANSIENT XTE J1701−462

We present Rossi X-Ray Timing Explorer and Swift observations made during the final three weeks of the 2006–2007 outburst of the super-Eddington neutron star (NS) transient XTE J1701–462, as well as Chandra and XMM-Newton observations covering the first ≃800 days of the subsequent quiescent phase. The source transitioned quickly from active accretion to quiescence, with the luminosity dropping by over 3 orders of magnitude in ≃13 days. The spectra obtained during quiescence exhibit both a thermal component, presumed to originate in emission from the NS surface, and a non-thermal component of uncertain origin, which has shown large and irregular variability. We interpret the observed decay of the inferred effective surface temperature of the NS in quiescence as the cooling of the NS crust after having been heated and brought out of thermal equilibrium with the core during the outburst. The interpretation of the data is complicated by an apparent temporary increase in temperature ≃220 days into quiescence, possibly due to an additional spurt of accretion. We derive an exponential decay timescale of ≃120+30−20 days for the inferred temperature (excluding observations affected by the temporary increase). This short timescale indicates a highly conductive NS crust. Further observations are needed to confirm whether the crust is still slowly cooling or has already reached thermal equilibrium with the core at a surface temperature of ≃125 eV. The latter would imply a high equilibrium bolometric thermal luminosity of ≃5 × 1033ergs−1 for an assumed distance of 8.8 kpc.
on Convolutional Neural Networks

This document discusses the derivation and implementation of convolutional neural networks (CNNs) [3, 4], followed by a few straightforward extensions. Convolutional neural networks involve many more connections than weights; the architecture itself realizes a form of regularization. In addition, a convolutional network automatically provides some degree of translation invariance. This particular kind of neural network assumes that we wish to learn filters, in a data-driven fashion, as a means to extract features describing the inputs. The derivation we present is specific to two-dimensional data and convolutions, but can be extended without much additional effort to an arbitrary number of dimensions.
Confirming the Distinctiveness of Complicated Grief from Depression and Anxiety Among Adolescents

Studies in adult populations have shown that symptoms of complicated grief (CG) constitute a form of bereavement-related distress distinct from symptoms of depression and anxiety. The purpose of this article is to replicate these findings in two samples of bereaved adolescents by investigating the latent structure of symptoms of CG, anxiety, and depression measured by self-report questionnaires. The first study (N = 245) focuses on one of the most prevalent losses in adolescence, namely the death of a grandparent. In the second study (N = 351) the authors further the examination of the distinctiveness question by inspecting not only the latent structure of CG, depression, and anxiety but also whether the distinctiveness of the emerging latent structure holds across subgroups of bereaved adolescents suffering different types of losses. Confirmatory factor analyses in both studies confirm the distinctiveness of CG from depression and anxiety in a younger population.
Missing feature reconstruction methods for robust speaker identification

In this study, we propose a reconstruction method to restore the degraded features for robust speaker identification. The proposed method is based on a hybrid generative model which consists of deep belief network (DBN) and restricted Boltzmann machine (RBM). Specifically, the noisy speech is firstly decomposed into time-frequency (T-F) representations. Then ideal binary mask (IBM) is computed to indicate each T-F point as reliable or unreliable. We reconstruct the unreliable ones by the proposed model iteratively. Finally, reconstructed feature is utilized to conventional speaker identification system. Experiments demonstrate that the proposed method achieves significant performance improvements over previous missing feature techniques under a wide range of signal-to-noise ratios.
Fast Generation of Large Scale Social Networks While Incorporating Transitive Closures

A key challenge in the social network community is the problem of network generation - that is, how can we create synthetic networks that match characteristics traditionally found in most real world networks? Important characteristics that are present in social networks include a power law degree distribution, small diameter, and large amounts of clustering. However, most current network generators, such as the Chung Lu and Kronecker models, largely ignore the clustering present in a graph and focus on preserving other network statistics, such as the power law distribution. Models such as the exponential random graph model have a transitivity parameter that can capture clustering, but they are computationally difficult to learn, making scaling to large real world networks intractable. In this work, we propose an extension to the Chung Lu random graph model, the Transitive Chung Lu (TCL) model, which incorporates the notion transitive edges. Specifically, it combines the standard Chung Lu model with edges that are formed through transitive closure (e.g., by connecting a 'friend of a friend'). We prove TCL's expected degree distribution is equal to the degree distribution of the original input graph, while still providing the ability to capture the clustering in the network. The single parameter required by our model can be learned in seconds on graphs with millions of edges, networks can be generated in time that is linear in the number of edges. We demonstrate the performance of TCL on four real-world social networks, including an email dataset with hundreds of thousands of nodes and millions of edges, showing TCL generates graphs that match the degree distribution, clustering coefficients and hop plots of the original networks.
c-Myc Regulates Proliferation and Fgf10 Expression in Airway Smooth Muscle after Airway Epithelial Injury in Mouse

During lung development, Fibroblast growth factor 10 (Fgf10), which is expressed in the distal mesenchyme and regulated by Wnt signaling, acts on the distal epithelial progenitors to maintain them and prevent them from differentiating into proximal (airway) epithelial cells. Fgf10-expressing cells in the distal mesenchyme are progenitors for parabronchial smooth muscle cells (PSMCs). After naphthalene, ozone or bleomycin-induced airway epithelial injury, surviving epithelial cells secrete Wnt7b which then activates the PSMC niche to induce Fgf10 expression. This Fgf10 secreted by the niche then acts on a subset of Clara stem cells to break quiescence, induce proliferation and initiate epithelial repair. Here we show that conditional deletion of the Wnt target gene c-Myc from the lung mesenchyme during development does not affect proper epithelial or mesenchymal differentiation. However, in the adult lung we show that after naphthalene-mediated airway epithelial injury c-Myc is important for the activation of the PSMC niche and as such induces proliferation and Fgf10 expression in PSMCs. Our data indicate that conditional deletion of c-Myc from PSMCs inhibits airway epithelial repair, whereas c-Myc ablation from Clara cells has no effect on airway epithelial regeneration. These findings may have important implications for understanding the misregulation of lung repair in asthma and COPD.
Who is Actually Harmed by Predatory Publishers

“ Predatory publishing” refers to conditions under which gold open-access academic publishers claim to conduct peer review and charge for their publishing services but do not, in fact, actually perform such reviews. Most prominently exposed in recent years by Jeffrey Beall, the phenomenon garners much media attention. In this article, we acknowledge that such practices are deceptive but then examine, across a variety of stakeholder groups, what the harm is from such actions to each group of actors. We find that established publishers have a strong motivation to hype claims of predation as damaging to the scholarly and scientific endeavour while noting that, in fact, systems of peer review are themselves already acknowledged as deeply flawed.
Searching and Browsing Tag Spaces Using the Semantic Tag Clustering Search Framework

Many of the existing cloud tagging systems are unable to cope with the syntactic and semantic tag variations during user search and browse activities. As a solution to this problem, we propose the Semantic Tag Clustering Search, a framework which is able to cope with these needs. The framework consists of two parts: removing syntactic variations and creating semantic clusters. For removing syntactic variations, we use the normalized Levenshtein distance and the cosine similarity measure based on tag co-occurrences. For creating semantic clusters, we improve an existing non-hierarchical clustering technique. Using our framework, we are able to find more clusters and achieve a higher precision than the original method.