Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexible Anonymous Network

Internet technologies have been designed from guidelines like the robustness principle also known as Postel's law. Jon Postel's law is described as: "Be conservative in what you do, be liberal in what you accept from others. " Fundamentally, it advises protocol designs to be tolerant with what they accept from the other peers. We propose to take a step back and wonder how the robustness principle could be revisited to support security requirements as well as unifying flexibility from specifications, protocol design and software implementations. Our goal would be to define a software architecture that offers the benefits of the robustness principle (i.e., efficient network services despite the presence of various software versions), while also guaranteeing that this robustness cannot be exploited by making sure that it is only used to support authentic evolution of the protocol specification.
Low-dimensional Embodied Semantics for Music and Language

Embodied cognition states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according to its subjective experience history, making this biological semantic machinery noisy with respect to the overall semantics inherent to media artifacts, such as music and language excerpts. We propose to represent shared semantics using low-dimensional vector embeddings by jointly modeling several brains from human subjects. We show these unsupervised efficient representations outperform the original high-dimensional fMRI voxel spaces in proxy music genre and language topic classification tasks. We further show that joint modeling of several subjects increases the semantic richness of the learned latent vector spaces.
Multicast Routing Algorithm of Network Lifetime

MaxNLMRGA (Max Network-Lifetime Multicast Routing Algorithm based on Genetic algorithm) for the NLMR (Network-Lifetime Multicast Routing) problem was presented. The genetic operators of this algorithm reduce the transmission cost and energy consumption of multicast trees, and mutation operator prolongs the network lifetime, thus accelerating the convergence speed of the algorithm. Experiment results show that the multicast tree found by this algorithm not only has the minimum transmission cost, but also has the longest network lifetime. Furthermore, this algorithm converges quickly.
Evaluating data-flow coverage in spectrum-based fault localization

Background: Debugging is a key task during the software development cycle. Spectrum-based Fault Localization (SFL) is a promising technique to improve and automate debugging. SFL techniques use control-flow spectra to pinpoint the most suspicious program elements. However, data-flow spectra provide more detailed information about the program execution, which may be useful for fault localization. Aims: We evaluate the effectiveness and efficiency of ten SFL ranking metrics using data-flow spectra. Method: We compare the performance of data- and control-flow spectra for SFL using 163 faults from 5 real-world open source programs, which contain from 468 to 4130 test cases. The data- and control-flow spectra types used in our evaluation are definition-use associations (DUAs) and lines, respectively. Results: Using data-flow spectra, up to 50% more faults are ranked in the top-15 positions compared to control-flow spectra. Also, most SFL ranking metrics present better effectiveness using data-flow to inspect up to the top-40 positions. The execution cost of data-flow spectra is higher than control-flow, taking from 22 seconds to less than 9 minutes. Data-flow has an average overhead of 353% for all programs, while the average overhead for control-flow is of 102%. Conclusions: The results suggest that SFL techniques can benefit from using data-flow spectra to classify faults in better positions, which may lead developers to inspect less code to find bugs. The execution cost to gather data-flow is higher compared to control-flow, but it is not prohibitive. Moreover, data-flow spectra also provide information about suspicious variables for fault localization, which may improve the developers’ performance using SFL.
Inferring surface shape from specular reflections

This thesis concerns the field of machine vision, in which computers are used to interpret pictures provided by television cameras. In particular it tackles the problems of interpreting pictures containing the glossy highlights produced by specular reflection. These specular highlights or specularities occur only where a glossy surface reflects the direct illumination of a light source almost straight into the camera's aperture as if the surface were a mirror. Practical vision systems ought to detect specularities for two reasons: firstly so that they will not be mistaken as surface markings, and secondly so that they can be used to infer shape information about the surface upon which they appear to lie. Schemes for both detection and shape inference are discussed and developed in this thesis. Surfaces which are not glossy Lambertian surfaces form images of a certain constrained class. The detection scheme described in this thesis identifies regions. of. an. image where the Lambertian constraints break. These regions are candidate specularities. In all, the evidence provided by tests on three separate constraints is combined to detect specularities. The scheme will work even for achromatic ('black and white') cameras. Two independent methods of inferring surface shape information are discussed. The first uses the manner in which the surface distorts the image of the light source. The second requires two cameras and uses the stereo disparity of the specularity relative to a nearby surface marking. The results obtained by the two methods can be combined to provide fuller information. Often the local surface curvature can be fully recovered. The detection and inference schemes have been implemented in a software system coded in C, together with a user friendly interface for interrogating the results.
Learning Fair Representations for Kernel Models

Fair representations are a powerful tool for establishing criteria like statistical parity, proxy non-discrimination, and equality of opportunity in learned models. Existing techniques for learning these representations are typically model-agnostic, as they preprocess the original data such that the output satisfies some fairness criterion, and can be used with arbitrary learning methods. In contrast, we demonstrate the promise of learning a model-aware fair representation, focusing on kernel-based models. We leverage the classical Sufficient Dimension Reduction (SDR) framework to construct representations as subspaces of the reproducing kernel Hilbert space (RKHS), whose member functions are guaranteed to satisfy fairness. Our method supports several fairness criteria, continuous and discrete data, and multiple protected attributes. We further show how to calibrate the accuracy tradeoff by characterizing it in terms of the principal angles between subspaces of the RKHS. Finally, we apply our approach to obtain the first Fair Gaussian Process (FGP) prior for fair Bayesian learning, and show that it is competitive with, and in some cases outperforms, state-of-the-art methods on real data.
A Survey of Location-Based Privacy Preserving

The explosive growth of location-detection devices along with wireless communications and mobile databases results in innovation applications based on users locations. However, recent concerns over how such potentially untrusted location servers can lead to user’s private information disclosure. This paper gives a comprehensive survey of location-based privacy preserving, and present the various location privacy models and techniques effective in the location anonymization based framework.
Apparent Spatial Memory Register and Oscillating Shape Transformations in Gold Nanostructures

Nano structures, namely, nano rings and nanoparticles, comprising two dimensional, crystalline gold atomic discs were produced on a sapphire substrate. Nano rings revealed several remarkable properties, namely, an apparent spatial orientation memory register, an apparent programmed homing instinct and action based on that instinct, usually observed only in living, biological systems. Momentary application of 100 nN force on a nanostructure, mother led every mother within the entire scanner area to increase its volume and create several daughter replicas. Over time, without the influence of any applied external force or stimulus, each daughter, of its own volition, transported itself back to its mother and recreated its original form. The results strongly implied temporal and spatial synchronization between the daughters and their respective mothers. The targeted movement of the daughters towards their mothers implied the existence of a special, yet unidentified new force. Momentary application of a weak force on a gold nanoparticle resulted in the spontaneous transformation of each nanoparticle into a complex and robot like Dalek 3dimensional form. Without the application of any additional external force or stimulus, we observed a remarkable display of oscillatory transformation between the robot-like forms and their mother nanoparticles lasting over 26 hours. The robot-like structures seem to replicate identical copies of themselves.
Protection Against Phishing Attacks: A Survey

The phishing problem has become the most dangerous form of online fraud to hit the Internet business. Many companies, organizations and software vendors have released their proprietary detection systems. This paper surveys these recent state of the art anti-phishing techniques. After analyzing and categorizing these methods, we list the comparison, merits and demerits of every mechanism. And in the last, we discussed the directions of the future anti-phishing systems’ development.
Gravitational waves as a probe of the extra dimension

We consider the Einstein-Hilbert action without cosmological constant in 5-dimensions and implement the Kaluza-Klein (KK) reduction by compactifying the fifth direction on a circle of small but finite radius. For non-zero compactification radius, the 4- dimensional spectrum contains massless and massive KK modes. For the massive KK modes, we retain four KK tensor and one KK scalar modes after a gauge fixing. We treat those massive KK modes as stochastic sources of gravitational wave (GW) with characteristic dependences of the frequencies on the size of the extra dimension. Using the observational bounds on the size of the extra dimension and on the characteristic strain, we make an order estimation on the frequencies and amplitudes of the massive KK modes that can contribute to the GW.
Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array operating in the frequency band of 30-80 MHz, measuring the radio emission of air-showers induced by ultra-high energy cosmic rays. Tunka-Rex is co-located with the TAIGA experiment in Siberia and consists of 63 antennas, 57 of them in a densely instrumented area of about 1km2. The signals from the air showers are short pulses, which have a duration of tens of nanoseconds and are recorded in traces of about 5{\mu}s length. The Tunka-Rex analysis of cosmic-ray events is based on the reconstruction of these signals, in particular, their positions in the traces and amplitudes. This reconstruction suffers at low signal-to-noise ratios, i.e. when the recorded traces are dominated by background. To lower the threshold of the detection and increase the efficiency, we apply advanced methods of signal reconstruction, namely matched filtering and deep neural networks with autoencoder architecture. In the present work we show the comparison between the signal reconstructions obtained with these techniques, and give an example of the first reconstruction of the Tunka-Rex signals obtained with a deep neural networks.
ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation

Semantic segmentation has been a hot topic across diverse research fields. Along with the success of deep convolutional neural networks, semantic segmentation has made great achievements and improvements, in terms of both urban scene parsing and indoor semantic segmentation. However, most of the state-of-the-art models are still faced with a challenge in discriminative feature learning, which limits the ability of a model to detect multi-scale objects and to guarantee semantic consistency inside one object or distinguish different adjacent objects with similar appearance. In this paper, a practical and efficient edge-aware neural network is presented for semantic segmentation. This end-to-end trainable engine consists of a new encoder-decoder network, a large kernel spatial pyramid pooling (LKPP) block, and an edge-aware loss function. The encoder-decoder network was designed as a balanced structure to narrow the semantic and resolution gaps in multi-level feature aggregation, while the LKPP block was constructed with a densely expanding receptive field for multi-scale feature extraction and fusion. Furthermore, the new powerful edge-aware loss function is proposed to refine the boundaries directly from the semantic segmentation prediction for more robust and discriminative features. The effectiveness of the proposed model was demonstrated using Cityscapes, CamVid, and NYUDv2 benchmark datasets. The performance of the two structures and the edge-aware loss function in ELKPPNet was validated on the Cityscapes dataset, while the complete ELKPPNet was evaluated on the CamVid and NYUDv2 datasets. A comparative analysis with the state-of-the-art methods under the same conditions confirmed the superiority of the proposed algorithm.
LINGUISTIC ASPECTS OF THE DEVELOPMENT OF COMPUTER SIGN LANGUAGE INTERPRETERS

The article is devoted to the actual direction of modern linguistics – describing the features of Russian sign language identified by the analysis of theoretical and empirical lexicographical sources, as well as in the process of communication with native «speakers» – people using Russian sign language as native language. Another purpose of the article is detecting and confirming the practical significance of such research – solving problems of social adaptation for hard of hearing people. The paper presents the results of linguistic research of Russian sign language that the developers of the computer Russian sign language translator rely on: peculiarities of lexis, word derivation, morphology and syntax of sign language for deaf citizens of the Russian Federation are described. Approaches to developing a system for computer sign language translation used by the employees of the Institute of Social Rehabilitation, Novosibirsk State Technical University, are justified in the paper. The results of the research can be interested to specialists studying and describing lexical and grammatical systems of Russian sign language, developers of the system for a twoway machine translation from Russian spoken language to Russian sign language and vice versa, sign language interpreters and teachers of sign language.
A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation

We propose a novel theoretical framework that generalizes algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning, and allows us to explore many combinations of different linkage criteria and cannot-link constraints. We prove the equivalence of existing clustering methods to some of those combinations, and introduce new algorithms for combinations which have not been studied. An extensive comparison is performed to evaluate properties of the clustering algorithms in the context of instance segmentation in images, including robustness to noise and efficiency. We show how one of the new algorithms proposed in our framework outperforms all previously known agglomerative methods for signed graphs, both on the competitive CREMI 2016 EM segmentation benchmark and on the CityScapes dataset.
The $\mathcal{N}_3=3\to \mathcal{N}_3=4$ enhancement of Super Chern-Simons theories in $D=3$, Calabi HyperK\"ahler metrics and M2-branes on the $\mathcal{C}(\mathrm{N^{0,1,0}})$ conifold

Considering matter coupled supersymmetric Chern-Simons theories in three dimensions we extend the Gaiotto-Witten mechanism of supersymmetry enhancement $\mathcal{N}_3=3\to \mathcal{N}_3=4$ from the case where the hypermultiplets span a flat HyperKahler manifold to that where they live on a curved one. We derive the precise conditions of this enhancement in terms of generalized Gaiotto-Witten identities to be satisfied by the tri-holomorphic moment maps. An infinite class of HyperKahler metrics compatible with the enhancement condition is provided by the Calabi metrics on $T^\star \mathbb{P}^{n}$. In this list we find, for $n=2$ the resolution of the metric cone on $\mathrm{N}^{0,1,0}$ which is the unique homogeneous Sasaki Einstein 7-manifold leading to an $\mathcal{N}_4=3$ compactification of M-theory. This leads to challenging perspectives for the discovery of new relations between the enhancement mechanism in $D=3$, the geometry of M2-brane solutions and also for the dual description of super Chern Simons theories on curved HyperKahler manifolds in terms of gauged fixed supergroup Chern Simons theories. The relevant supergroup is in this case $\mathrm{SU(3|N)}$ where $\mathrm{SU(3)}$ is the flavor group and $\mathrm{U(N)}$ is the color group.
Time-varying Extinction, Polarization, and Colors of Type Ia Supernovae due to Rotational Disruption of Dust Grains

Observations toward type Ia supernovae (SNe Ia) frequently report an unusually low total-to-selective extinction ratio (RV < 2) and a small peak wavelength of polarization (λmax < 0.4 μm). Hoang et al. proposed that the dominance of small grains near SNe Ia due to RAdiative Torque Disruption (RATD) can explain the puzzle. To test this scenario, we model the dust extinction and polarization of SNe Ia, accounting for grain disruption and alignment by radiative torques and different grain tensile strengths from . We find that RV decreases from ∼3.1 to ∼1.5 after disruption time tdisr < 40 days for clouds at a distance d < 4 pc from SNe Ia. We then calculate the observed lightcurves of SNe Ia and find that their colors change with time, due to varying dust extinction. The peak wavelength λmax also decreases from ∼0.55 to ∼0.15 μm over an alignment time of talign < 10 days, due to the enhanced alignment of small grains. By fitting the theoretical polarization with the Serkowski law characterized by the K and λmax parameters, we find that K increases when large grains are disrupted by RATD, which is consistent with the unusual K versus λmax relationship of SNe Ia. Nevertheless, an accurate measurement of K and λmax is challenging, due to atmospheric effects and potential contamination of polarization by Rayleigh scattering by circumstellar matter. Our results demonstrate the importance of RATD for time-dependent extinction, polarization, and colors of SNe Ia.
Latent Optimization for Non-adversarial Representation Disentanglement

Disentanglement between pose and content is a key task for artificial intelligence and has attracted much research interest. Current methods for disentanglement include adversarial training and introducing cycle constraints. In this work, we present a novel disentanglement method which does not use adversarial training, achieving state-of-the-art performance. Our method uses latent optimization of an architecture borrowed from style-transfer, to enforce separation of pose and content. We overcome the test generalization issues of latent optimization, by a novel two-stage approach. In extensive experiments, our method is shown to achieve better disentanglement performance than both adversarial and non-adversarial methods that use the same level of supervision.
Dark matter capture by the Sun: revisiting velocity distribution uncertainties

Among the diﬀerent strategies aiming to detect WIMP dark matter (DM), a neutrino signal coming from the Sun would be a smoking gun. This possibility relies on the DM capture by the Sun driven by the local DM distribution assumptions: the local mass density and the velocity distribution. In this context, we revisit those astrophysical hypotheses (also relevant for direct detection). We focus especially on the DM velocity distribution considering diﬀerent possibilities beyond the popular Maxwellian distribution. Namely, some alternatives can be considered through analytical approaches and by looking into cosmological simulations of spiral galaxies. Most of the ﬁtting formulas used to constrain the local velocity distribution function fail to describe the peak and the high velocity tail of the velocity distribution observed in simulations, the latter being improved when adding the local escape velocity of DM into the benchmark ﬁtting models. In addition we test the predictions by the Eddington inversion method and also illustrate the importance of the galactic dynamical history. We estimate the resulting uncertainties on the DM capture rate by the Sun and conclude that diﬀerent velocity distributions will aﬀect the capture rate of DM by the Sun up to a 15 − 20% . On top of that, the calculation of the intrinsic variance of the capture rate leads to poorly controlled uncertainties especially for high WIMP masses ( > 30 GeV) raising concerns about the capture scenario.
Parallel Reinforcement Learning Systems Using Exploration Agents

We propose a new strategy for parallel reinforcement learning ; using this strategy, the optimal value function and policy can be constructed more quickly than by using traditional strategies. We define two types of agents : the exploitation agents and the exploration agents. The exploitation agents select actions mainly for exploitation, and the exploration agents concentrate on exploration using the extended k-certainty exploration method. These agents learn in the same environment in parallel and combine each value function periodically. By using this strategy, the construction of the optimal value function is expected, and the optimal actions can be selected by the exploitation agents quickly. The experimental results of the mobile robot simulation showed the availability of our method.
System Administrators Prefer Command Line Interfaces, Don't They? An Exploratory Study of Firewall Interfaces

A graphical user interface (GUI) represents the most common option for interacting with computer systems. However, according to the literature system administrators often favor command line interfaces (CLIs). The goal of our work is to investigate which interfaces system administrators prefer, and which they actually utilize in their daily tasks. We collected experiences and opinions from 300 system administrators with the help of an online survey. All our respondents are system administrators, who work or have worked with firewalls. Our results show that only 32% of the respondents prefer CLIs for managing firewalls, while the corresponding figure is 60% for GUIs. We report the mentioned strengths and limitations of each interface and the tasks for which they are utilized by the system administrators. Based on these results, we provide design recommendations for firewall interfaces.