Miracle-dz/UniFAR-FTUs huggingface dataset:
Textual Graphical Password Scheme against Shoulder Surfing Attack

Schemes like conventional password schemes such as textual password scheme, graphical scheme are commonly used for authentication. But these schemes are vulnerable to dictionary attack, shoulder surfing attack, accidental login. Hence the text-based shoulder surfing resistant graphical password schemes have been proposed. These existing schemes are not secure and efficient enough and have high failure rate. The text-based shoulder surfing resistant graphical password scheme is improved by using colors. In addition one time password is also used. So it has become more secure. User can easily login to the system. Unauthorized user cannot get the password easily. Hence this scheme provide protection against the shoulder surfing.
Systematic Mapping Protocol Feature Modeling Tools

The customers and users need for new products and services according to high-quality standards have increased in the last time. In that sense, the production processes must be aligned with the organization and development process in order to achieve this goal. The aim of this paper is to synthesize the current state of the research reported in the literature regarding the application domain, underlying model, origin, degree of empirical validation and quality of existing feature modeling tools used in SPL. Therefore, this technical report presents the protocol definition for a systematic mapping study (SMS) that we will conduct to identify and assess the set of relevant papers on feature model tools.
Neural Shufﬂe-Exchange Networks − Sequence Processing in O( n log n ) Time

A key requirement in sequence to sequence processing is the modeling of long range dependencies. To this end, a vast majority of the state-of-the-art models use attention mechanism which is of O( n 2 ) complexity that leads to slow execution for long sequences. We introduce a new Shufﬂe-Exchange neural network model for sequence to sequence tasks which have O(log n ) depth and O( n log n ) total complexity. We show that this model is powerful enough to infer efﬁcient algorithms for common algorithmic benchmarks including sorting, addition and multiplication. We evaluate our architecture on the challenging LAMBADA question answering dataset and compare it with the state-of-the-art models which use attention. Our model achieves competitive accuracy and scales to sequences with more than a hundred thousand of elements. We are conﬁdent that the proposed model has the potential for building more efﬁcient architectures for processing large interrelated data in language modeling, music generation and other application domains.
Art Metrics

Success in art markets is difficult to quantify objectively, as it also relies on complex social networks and exchanges of reputation among different actors of the system. We discuss the general task of developing art metrics that are able to capture the different roles actors play in art markets, in particular artists and collectors, are time-aware and efficient to account for the dynamic nature of such markets, and are predictive of future success. As a first contribution in this direction, we propose a method to capture the mutually reinforcing role of artists and collectors via a time-aware extension of Kleinberg’s HITS method, originally developed for the Web. We apply the method to a dataset comprising all the events of the crypto art gallery SuperRare during its first year of existence. Crypto art is limited-edition, collectible, and tradable digital art cryptographically registered on a blockchain. This very recent artistic movement, sharing several approaches and motivations with conceptual art, is producing data at an unprecedented level of detail when compared to the traditional art market. The proposed method is predictive of future success and accurately captures the roles of artists and collectors.
Femtosecond imbalanced time-stretch spectroscopy for ultrafast gas detection

Dual-comb spectroscopy is a promising method for precise optical spectrum analysis with fast data acquisition speed. Here, avoiding using a dual-comb source, femtosecond imbalanced time-stretch spectroscopy with a simple optical layout is proposed and demonstrated. Time-stretch interferometry from one femtosecond laser builds mapping from the optical frequency domain to the radio frequency regime. In experiment, the absorption line of a hydrogen cyanide cell is encoded in the probing arm of a Mach–Zehnder interferometer (MZI). The down-converted radio frequency comb is transformed from a periodically chirped waveform, which is the interferogram of the MZI with different dispersion values on two arms. In a single measurement, the optical frequency comb with a span of 112.5 GHz is down-converted to a range of about 20.8 GHz in the radio frequency domain with a comb spacing equal to the laser repetition frequency of 100 MHz. By turning the optical filter, a spectrum range around 2 THz is analyzed. The acquired optical spectrum resolution is 540 MHz.
Long range order in atomistic models for solids

The emergence of long-range order at low temperatures in atomistic systems with continuous symmetry is a fundamental, yet poorly understood phenomenon in Physics. To address this challenge we study a discrete microscopic model for an elastic crystals with dislocations in three dimensions, previously introduced by Ariza and Ortiz. The model is rich enough to support some realistic features of three-dimensional dislocation theory, most notably grains and the Read-Shockley law for grain boundaries, which we rigorously derive in a simple, explicit, geometry. We analyze the model at positive temperatures, in terms of a Gibbs distribution with energy function given by the Ariza-Ortiz Hamiltonian plus a contribution from the dislocation cores. Our main result is that the model exhibits long range positional order at low temperatures. The proof is based on the tools of discrete exterior calculus, together with cluster expansion techniques.
Heavy metals in intermediate He-rich hot subdwarfs: the chemical composition of HZ 44 and HD 127493

Context. Hot subluminous stars can be spectroscopically classified as subdwarf B (sdB) and O (sdO) stars. While the latter are predominantly hydrogen deficient, the former are mostly helium deficient. The atmospheres of most sdOs are almost devoid of hydrogen, whereas a small group of hot subdwarf stars of mixed H/He composition exists, showing extreme metal abundance anomalies. Whether such intermediate helium-rich (iHe) subdwarf stars provide an evolutionary link between the dominant classes is an open question. Aims. The presence of strong Ge, Sn, and Pb lines in the UV spectrum of HZ 44 suggests a strong enrichment of heavy elements in this iHe-sdO star and calls for a detailed quantitative spectral analysis focusing on trans-iron elements. Methods. Non-local thermodynamical equilibrium model atmospheres and synthetic spectra calculated with TLUSTY/SYNSPEC were combined with high-quality optical, ultraviolet (UV), and far-UV (FUV) spectra of HZ 44 and its hotter sibling HD 127493 to determine their atmospheric parameters and metal abundance patterns. Results. By collecting atomic data from the literature we succeeded in determining the abundances of 29 metals in HZ 44, including the trans-iron elements Ga, Ge, As, Se, Zr, Sn, and Pb and providing upper limits for ten other metals. This makes it the best-described hot subdwarf in terms of chemical composition. For HD 127493 the abundance of 15 metals, including Ga, Ge, and Pb and upper limits for another 16 metals were derived. Heavy elements turn out to be overabundant by one to four orders of magnitude with respect to the Sun. Zr and Pb are among the most enriched elements. Conclusions. The C, N, and O abundance for both stars can be explained by the nucleosynthesis of hydrogen burning in the CNO cycle along with the stars’ helium enrichment. On the other hand, the heavy-element anomalies are unlikely to be caused by nucleosynthesis. Instead diffusion processes are evoked, with radiative levitation overcoming gravitational settlement of the heavy elements.
Searching for the QCD Critical Point with Net-Proton Number Fluctuations

Net-proton number fluctuations can be measured experimentally and, hence, provide a source of important information about the matter created during relativistic heavy ion collisions. Particularly, they may give us clues about the conjectured QCD critical point. In this work, the beam-energy dependence of ratios of the first four cumulants of the net-proton number is discussed. These quantities are calculated using a phenomenologically motivated model in which critical mode fluctuations couple to protons and antiprotons. Our model qualitatively captures both the monotonic behavior of the lowest-order ratio, as well as the non-monotonic behavior of higher-order ratios, as seen in the experimental data from the STAR Collaboration. We also discuss the dependence of our results on the coupling strength and the location of the critical point.
Topological Phase Transition of Non‐Hermitian Crosslinked Chain

The contents of topological classification of matter are enriched by non‐Hermiticity, such as exceptional points, bulk‐edge correspondence, and skin effects. Physically, gain and loss can be introduced by imaginary on‐site potentials of lattice Hamiltonians, and the topological phase transition for a cross‐linked chain in the presence of such non‐Hermiticity is investigated. The topological phase diagram in terms of a winding number is obtained analytically with phase boundaries coinciding with the surfaces of exceptional points. The topologically original edge states with distribution mainly at the joints between domains of different phases are protected even for long chains. The non‐Hermitian topological feature can also be reflected by vortex structures in the vector fields of complex eigenenergies, expected values of Pauli matrices, and trajectories of these quantities. This model may be implemented in coupled photonic crystals, fermions trapped in optical lattice, or non‐Hermitian electrical‐circuit lattices, and the edge states are immune to various kinds of disorders until topological phase transition occurs. This work gives insight into the influence of non‐Hermiticity on topological phase of matter.
3.8 μm Imaging of 400–600 K Brown Dwarfs and Orbital Constraints for WISEP J045853.90+643452.6AB

Half of the energy emitted by late-T- and Y-type brown dwarfs emerges at 3.5 ≤ λ μm ≤ 5.5. We present new L′ (3.43 ≤ λ μm ≤ 4.11) photometry obtained at the Gemini North telescope for nine late-T and Y dwarfs, and synthesize L′ from spectra for an additional two dwarfs. The targets include two binary systems that were imaged at a resolution of 0. ″25. One of these, WISEP J045853.90+643452.6AB, shows significant motion, and we present an astrometric analysis of the binary using Hubble Space Telescope, Keck Adaptive Optics, and Gemini images. We compare λ ∼ 4 μm observations to models, and find that the model fluxes are too low for brown dwarfs cooler than ∼700 K. The discrepancy increases with decreasing temperature, and is a factor of ∼2 at Teff = 500 K and ∼4 at Teff = 400 K. Warming the upper layers of a model atmosphere generates a spectrum closer to what is observed. The thermal structure of cool brown dwarf atmospheres above the radiative-convective boundary may not be adequately modeled using pure radiative equilibrium; instead heat may be introduced by thermochemical instabilities (previously suggested for the L- to T-type transition) or by breaking gravity waves (previously suggested for the solar system giant planets). One-dimensional models may not capture these atmospheres, which likely have both horizontal and vertical pressure/temperature variations.
Evaluating the Utility of Document Embedding Vector Difference for Relation Learning

Recent work has demonstrated that vector offsets obtained by subtracting pretrained word embedding vectors can be used to predict lexical relations with surprising accuracy. Inspired by this finding, in this paper, we extend the idea to the document level, in generating document-level embeddings, calculating the distance between them, and using a linear classifier to classify the relation between the documents. In the context of duplicate detection and dialogue act tagging tasks, we show that document-level difference vectors have utility in assessing document-level similarity, but perform less well in multi-relational classification.
Automating concept-drift detection by self-evaluating predictive model degradation

A key aspect of automating predictive machine learning entails the capability of properly triggering the update of the trained model. To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised. In this paper, we propose a novel methodology to automatically detect prediction-quality degradation of machine learning models due to class-based concept drift, i.e., when new data contains samples that do not fit the set of class labels known by the currently-trained predictive model. Experiments on synthetic and real-world public datasets show the effectiveness of the proposed methodology in automatically detecting and describing concept drift caused by changes in the class-label data distributions.
Scalar-on-function local linear regression and beyond

It is common to want to regress a scalar response on a random function. This paper presents results that advocate local linear regression based on a projection as a nonparametric approach to this problem. Our asymptotic results demonstrate that functional local linear regression outperforms its functional local constant counterpart. Beyond the estimation of the regression operator itself, local linear regression is also a useful tool for predicting the functional derivative of the regression operator, a promising mathematical object in its own right. The local linear estimator of the functional derivative is shown to be consistent. For both the estimator of the regression functional and the estimator of its derivative, theoretical properties are detailed. On simulated datasets we illustrate good finite-sample properties of the proposed methods. On a real data example of a single-functional index model, we indicate how the functional derivative of the regression operator provides an original, fast and widely applicable estimation method.
Approximate Counting CSP Seen from the Other Side

In this article, we study the complexity of counting Constraint Satisfaction Problems (CSPs) of the form #CSP(C, -), in which the goal is, given a relational structure A from a class C of structures and an arbitrary structure B, to find the number of homomorphisms from A to B. Flum and Grohe showed that #CSP(C, -) is solvable in polynomial time if C has bounded treewidth [FOCS’02]. Building on the work of Grohe [JACM’07] on decision CSPs, Dalmau and Jonsson then showed that if C is a recursively enumerable class of relational structures of bounded arity, then, assuming FPT≠ #W[1], there are no other cases of #CSP(C, -) solvable exactly in polynomial time (or even fixed-parameter time) [TCS’04]. We show that, assuming FPT ≠ W[1] (under randomised parameterised reductions) and for C satisfying certain general conditions, #CSP(C,-) is not solvable even approximately for C of unbounded treewidth; that is, there is no fixed parameter tractable (and thus also not fully polynomial) randomised approximation scheme for #CSP(C, -). In particular, our condition generalises the case when C is closed undertaking minors.
Subgroup Discovery on Numeric and Ordinal Targets, with an Application to Biological Data Aggregation

Subgroup discovery can generate descriptive patterns given a nominal or binary target variable. To do so, subgroup discovery uses quality measures that define the quality of a subgroup given the target values of the subgroup. However, not all problems are nominal in nature. More specifically, data can be either ranked and/or the target can be continuous. In the past, non-nominal targets needed to be discretized. Discretization can lead to less powerful or even faulty patterns, due to loss of information. Quality measures capable of dealing with continuous and even ordinal targets directly can help to overcome these issues. In this research, such quality measures are investigated and tested on the problem of gene set enrichment. Here, the goal is to find common functional knowledge on ranked genes. In this case, the genes are ranked according to their relevance to neuroblastoma, the most common extracranial solid tumour found in children. The results of the experiments are promising and show that subgroup discovery can be an addition to conservative research methods in for instance biology.
Channel Capacity Enhancement of SWIPT based CNOMA Downlink Transmission with Orbital Angular Momentum over Rician Fading Channel

In this paper, the integration of cooperative non-orthogonal multiple access (CNOMA) and power splitting based simultaneous wireless information and power transfer (SWIPT) is proposed to improve the spectral and energy efficiency. Moreover, different orbital angular momentum (OAM) based signals are also considered in this paper to transmit additional symbols to cell center user (CCU) and cell edge user (CEU). In the proposed technique, CCU is used as energy-constrained relay to forward signal to CEU. CCU harvests energy from the transmitted signal by the BS using the power splitting technique. The additional symbols are transmitted from the BS to CCU and CEU by utilizing different modes of OAM. This paper investigates the ergodic sum capacity (ESC) of the proposed technique along with analytical derivations over Rician fading channels. Finally, the performance in terms of ESC and energy efficiency over conventional technique compared to conventional multiple access is demonstrated by analysis and simulation.
Self-optimizing traffic classification framework

Klasifikacia sieťovej prevadzky je doležitou sucasťou spravy siete s ohľadom na QoS a bezpecnostný monitoring. Praca popisuje framework pre navrh algoritmov pre klasifikaciu sieťovej prevadzky založený na statistických vlastnostiach tokov. Stavia na výsledkoch algoritmu SPID a využiva viacurovňový clustering so samostatnými klasifikatormi. V praci su tiež uvedených niekoľko nových vlastnosti tokov a optimalizacia struktury a parametrov algoritmu genetickým algoritmom. Efektivita navrhnuteho riesenia je vyhodnotena na zazname realnej sieťovej prevadzky a porovnana so algoritmom SPID. Predbežne výsledky ukazuju zlepsenu presnosť s výrazným zrýchlenim klasifikacie.
Wave Heating in Simulated Multistranded Coronal Loops

It has been found that the Kelvin–Helmholtz instability (KHI) induced by both transverse and torsional oscillations in coronal loops can reinforce the effects of wave heating. In this study, we model a coronal loop as a system of individual strands, and we study wave heating effects by considering a combined transverse and torsional driver at the loop footpoint. We deposit the same energy into the multistranded loop and an equivalent monolithic loop, and then observe a faster increase in the internal energy and temperature in the multistranded model. Therefore, the multistranded model is more efficient in starting the heating process. Moreover, higher temperature is observed near the footpoint in the multistranded loop and near the apex in the monolithic loop. The apparent heating location in the multistranded loop agrees with the previous predictions and observations. Given the differences in the results from our multistranded loop and monolithic loop simulations, and given that coronal loops are suggested to be multistranded on both theoretical and observational grounds, our results suggest that the multistrandedness of coronal loops needs to be incorporated in future wave-based heating mechanisms.
Consistent bootstrap one-step prediction region for state-vector in state space model.

In this paper, we propose a bootstrap algorithm to construct non-parametric prediction region for a simplified state-space model and provide theoretical proof for its consistency. Besides, we introduce prediction problem under the situation that innovation depends on previous observations and extend definition in \cite{PAN20161} to a broader class of models. Numerical results show that performance of proposed bootstrap algorithm depends on smoothness of data and deviation of last observation and state variable. Results in this paper can be adjusted to other classical models, like stochastic volatility model, exogenous time series model and etc.
Chinese Sign Language Recognition Research Using SIFT-BoW and Depth Image Information

Introducing the depth image information into sign language recognition research,a Chinese sign language recognition method based on DI_CamShift(Depth Image CamShift)and SIFT-BoW(Scale Invariant Feature TransformBag of Words)was presented.It uses Kinect as the video capture device to obtain both of the color video and depth image information of sign language.First,it calculates spindle direction angle and mass center position of the depth image correctly tracks gesture by adjusting the search window.Second,an Ostu algorithm based on depth integral image is used to gesture segmentation,and the SIFT features are extracted.Finally,it builds SIFT-BoW as the feature of sign language and uses SVM for recognition.The experimental results show that the best recognition rate of single manual alphabet can reach 99.87%,while the average recognition rate is 96.21%.
The Key Roles of Maintainability in an Ontology for System Qualities

In our INCOSE IS 2015 paper, “An Initial Ontology for System Qualities,” (SQs), we provided an IDEF5 class hierarchy of upper‐level SQs, where the top level reflected classes of stakeholder value propositions (Mission Effectiveness, Resource Utilization, Dependability, Flexibility), and the next level identified means‐ends enablers of the higher‐level SQs. In experimenting with, refining and formalizing the ontology, we focused on a depth‐first approach on a chosen SQ: Maintainability. It is key to reducing 75% of most systems’ life cycle costs. Also Maintainability plays key roles in three of the four top‐level SQs: Resource Utilization (now called Life Cycle Efficiency) Dependability, and Flexibility (now called Changeability). Dependability also needs Maintainability to relate Reliability to Availability; and Changeability also needs Maintainability to address new system challenges and opportunities. This paper summarizes resulting changes in the SQ ontology, and also provides examples of Maintainability need and use, quantitative relations where available, and summaries and references on improved practices.
ICT Systems Security and Privacy Protection: 30th IFIP TC 11 International Conference, SEC 2015, Hamburg, Germany, May 26-28, 2015

This book constitutes the refereed proceedings of the 30th IFIP TC 11 International Information Security and Privacy Conference, SEC 2015, held in Hamburg, Germany, in May 2015. The 42 revised full papers presented were carefully reviewed and selected from 212 submissions. The papers are organized in topical sections on privacy, web security, access control, trust and identity management, network security, security management and human aspects of security, software security, applied cryptography, mobile and cloud services security, and cyber-physical systems and critical infrastructures security.