Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel classification scheme for 802.11 WLAN active attacking traffic patterns

In 802.11 WLANs, active intrusion attacks on the MAC layer causes changes in the management frame distributions. This paper focuses on detecting intrusions by analyzing the management traffic patterns. Experimental results are presented that describe the patterns generated by two well-known active attacks on 802.11 WLANs: deauthentication denial-of service (DoS) and man-in-the-middle (MITM). By clustering the management frame bursts over a certain period of time, we observe that the active attacking traffic patterns can be classified through calculation of a cluster content value (CCV). Our results show that when any station in a WLAN experiences clustering in its management traffic distribution, the CCV can be used to detect and classify the attacks the station is experiencing
Balance control of a novel power transmission line inspection robot

This paper presents several methods to maintain the stability of a new designed inspection robot when walking on the power transmission line and overcoming the obstacle on the line. The new dual-arm platform has 3 DOFs of each arm to fulfill rotating, lifting and lowering and moving on the line with a wheel. With a novel mechanism the robot can control the position of its center of gravity flexibly. The model of the power transmission line inspection robot is established for stability control. A FZMP controller is used to maintain the coincidence of the projection of the center of gravity and contact point, which is the necessary condition of the robot's stability. A look-ahead control algorithm is introduced to deal with the overshooting and vibration caused by sharp corner or high curvature in the joint space of the planned path to overcome an obstacle. The paper ends with the presentation of an experiment results.
Systematic polysemy and the count-mass distinction *

This paper investigates the type of systematic sense alternation that rests on the count-mass distinction in English. Computational semantic approaches often suggest treating such sense alterations as being generated on the basis of a set of lexical inference rules, yielding, for instance, for an animal-denoting term, a meat sense ( ̳We‘re having rabbit for dinner‘), a fur sense ( ̳Annie is wearing rabbit‘), and a general ̳stuff‘ sense ( ̳After a tractor had run over the body, there was rabbit splattered all over the yard‘). I question this rule-based account on the basis that it doesn‘t provide the interpretive flexibility required to account for the data, and argue that, although the sense alternations in question clearly have a linguistic component, pragmatics is playing a crucial role in giving rise to such sense alternations. I propose an alternative analysis, where the countmass distinction is seen as a primarily conceptual-ontological distinction being reflected in a syntactic distinction at the level of NPs (rather than as a property of nouns). On this account, count-mass syntax provides an instruction to the pragmatic system that the concept encoded by the NP should be construed as denoting either an individual or an unindividuated entity. On the basis of such underspecified inputs, highly activated encyclopaedic and logical information associated with the concept, and contextual assumptions derived from the utterance situation, the relevance-optimising pragmatic system operates to yield the speaker-intended concept.
Incremental Constraint Projection-Proximal Methods for Nonsmooth Convex Optimization

We consider convex optimization problems with structures that are suitable for stochastic sampling. In particular, we focus on problems where the objective function is an expected value or is a sum of a large number of component functions, and the constraint set is the intersection of a large number of simpler sets. We propose an algorithmic framework for projection-proximal methods using random subgradient/function updates and random constraint updates, which contain as special cases several known algorithms as well as new algorithms. To analyze the convergence of these algorithms in a unied manner, we prove a general coupled convergence theorem. It states that the convergence is obtained from an interplay between two coupled processes: progress towards feasibility and progress towards optimality. Moreover, we consider a number of typical sampling/randomization schemes for the subgradients/component functions and the constraints, and analyze their performance using our unied convergence framework.
Nitrogen addition shifts the microbial community in the rhizosphere of Pinus tabuliformis in Northwestern China

Atmospheric nitrogen (N) deposition profoundly alters the soil microbial communities and will thus affect nutrient cycles. The effects of N availability on microbial community, however, are not clear. We used PLFA analysis to evaluate the effects of a gradient of N addition (0, 2.8, 5.6, 11.2, and 22.4 g N m-2 y-1) for three years on the rhizospheric microbial community of Pinus tabuliformis seedlings. The main factors influencing the community were quantified using structural equation modelling and redundancy analysis. At the microbial-community level, N addition increased the total phospholipid fatty acids content by increasing the dissolved organic carbon (DOC) and root biomass. Increases in soil microbial biomass carbon and N, however, was attributed to the increased DOC, N content and decreased pH. At the microbial-groups level, Fungal, arbuscular mycorrhizal fungal (AMF), gram-positive bacterial (GP) abundances and the GP:GN ratio first increased and then decreased with N addition. Nitrogen addition increased the abundances of bacteria, fungi, and actinomycetes mainly by increasing the DOC content and decreasing root biomass. Additionally, the decrease of pH and ammonium N caused by N addition increased the fungal abundances and reduced actinomycete abundances, respectively. Nitrogen addition shifted the rhizospheric microbial community mainly by altering the DOC content and root biomass. The current rate of N deposition (2.5 g N m-2 y-1) benefits plant growth and increases the abundances of fungi, arbuscular mycorrhizal fungi, GP, actinomycetes and the GP:GN ratio.
Task Allocation Algorithm Based on Robot Ability and Relevance with Group Collaboration in a Robot Team

Task allocation for mobile robots team is a crucial issue before performing a certain task. The algorithm we propose here is named RARGC—a task allocation algorithm based on Robot Ability and Relevance with Group Collaboration, where robot ability is weighted by belief degree, relevance stands for a fresh concept of “history relevance” between every two robots to establish reasonable groups for better collaboration, and group collaboration includes inter and inner group help strategy be adopted as different nodes being failures in unknown environment. RARGC emphasizes the role of “agent node” in each group that is responsible for task competition and group leadership; formation maintenance as well as task execution with changing agents. Simulation on Player/Stage shows that the mechanism is feasible and valid.
3D facial geometry reconstruction using patch database

3D facial shape reconstruction in the wild environments is an important research task in the field of CG and CV. This is because it can be applied to a lot of products, such as 3DCG video games and face recognition. One of the most popular 3D facial shape reconstruction techniques is 3D Model-based approach. This approach approximates a facial shape by using 3D face model, which is calculated by principal component analysis. [Blanz and Vetter 1999] performed a 3D facial reconstruction by fitting points from facial feature points of an input of single facial image to vertex of template 3D facial model named 3D Morphable Model. This method can reconstruct a facial shape from a variety of images which include different lighting and face orientation, as long as facial feature points can be detected. However, representation quality of the result depends on the number of 3D model resolution.
Firm performance and the axis of errors

Firms sometimes fail to capture opportunities, fail to imitate perfectly-imitable resources, and do not solve their solvable problems. The persistence of errors creates intra-industry performance variation that is usually attributed to the competitive advantages of successful firms. However, firms compete on two axes: the axis of competitive advantage, where performance is driven by the inimitable resources and capabilities of high-performing firms; and the axis of errors, where performance is driven by failures to attend to the activities, resources and opportunities that are equally available to all firms. This paper investigates the latter, showing how errors produce performance variation not attributable to competitive advantages, and discussing their consequences for strategy theory, empirical research and management practice.
UT-STPD-1 / 03 The Peccei – Quinn Field as Curvaton

A simple extension of the minimal supersymmetric standard model which naturally and simultaneously solves the strong CP and μ problems via a PecceiQuinn and a continuous R symmetry is considered. This model is supplemented with hybrid inflation and leptogenesis, but without taking the specific details of these scenarios. It is shown that the Peccei-Quinn field can successfully act as a curvaton generating the total curvature perturbation in the universe in accord with the cosmic background explorer measurements. A crucial phenomenon, which assists us to achieve this, is the ‘tachyonic amplification’ of the perturbation acquired by this field during inflation if the field, in its subsequent evolution, happens to be stabilized for a while near a maximum of the potential. In this case, the contribution of the field to the total energy density is also enhanced (‘tachyonic effect’), which helps too. The cold dark matter in the universe consists, in this model, mainly of axions which carry an isocurvature perturbation uncorrelated with the total curvature perturbation. There are also lightest sparticles (neutralinos) which, like the baryons, originate from the inflationary reheating and, thus, acquire an isocurvature perturbation fully correlated with the curvature perturbation. So, the overall isocurvature perturbation has a mixed correlation with the adiabatic one. It is shown that the presently available bound on such an isocurvature perturbation from cosmic microwave background radiation and other data is satisfied. Also, the constraint on the non-Gaussianity of the curvature perturbation obtained from the recent Wilkinson microwave anisotropy probe data is fulfilled thanks to the ‘tachyonic effect’.
Not GABA But Glycine Mediates Segmental, Propriospinal, and Bulbospinal Postsynaptic Inhibition in Adult Mouse Spinal Forelimb Motor Neurons

The general view is that both glycine (Eccles, 1964) and GABA (Curtis and Felix, 1971) evoke postsynaptic inhibition in spinal motor neurons. In newborn or juvenile animals, there are conflicting results showing postsynaptic inhibition in motor neurons by corelease of GABA and glycine (Jonas et al., 1998) or by glycine alone (Bhumbra et al., 2012). To resolve the relative contributions of GABA and glycine to postsynaptic inhibition, we performed in vivo intracellular recordings from forelimb motor neurons in adult mice. Postsynaptic potentials evoked from segmental, propriospinal, and bulbospinal systems in motor neurons were compared across four different conditions: control, after gabazine, gabazine followed by strychnine, and strychnine alone. No significant differences were observed in the proportion of IPSPs and EPSPs between control and gabazine conditions. In contrast, EPSPs but not IPSPs were recorded after adding strychnine with gabazine or administering strychnine alone, suggesting an exclusive role for glycine in postsynaptic inhibition. To test whether the injected (intraperitoneal) dose of gabazine blocked GABAergic inhibitory transmission, we evoked GABAA receptor-mediated monosynaptic IPSPs in deep cerebellar nuclei neurons by stimulation of Purkinje cell fibers. No monosynaptic IPSPs could be recorded in the presence of gabazine, showing the efficacy of gabazine treatment. Our results demonstrate that, in the intact adult mouse, the postsynaptic inhibitory effects in spinal motor neurons exerted by three different systems, intrasegmental and intersegmental as well as supraspinal, are exclusively glycinergic. These findings emphasize the importance of glycinergic postsynaptic inhibition in motor neurons and challenge the view that GABA also contributes.
Application of a new Adaptive Kalman Filitering algorithm in initial alignment of INS

In order to prevent the filtering divergence and improve real — time of the system, Proposing a new type of sage-husa — based adaptive filtering algorithm on initial alignment method of inertial. The regular kalman filter algorithm suitable for using in noise statistical characteristics known circumstances, but most of the noise statistical characteristics unknown. To achieve the best filiter effect, Adaptive Kalman Filitering (AKF) algorithm use observed data and automatic on-line estimation and correction of noise statistical characteristics. Simulation results show that the algorithm for improving alignment accuracy.
An Automated System To Classify Alloy Steel Surface Using Contourlet Transform

Surface defect detection of metallic surfaces is a major challenge in any manufacturing industry. In this paper, an automated system to classify alloy steel surface based on contourlet transform is presented. As the contourlet transform is a multi resolution analysis, texture present in alloy steel surface is represented in various scales and directions. The image is decomposed at various scales and directions and the energy features are extracted. By analyzing the energies from the trained images, the best set that distinguishes the surface into defect or non defect is chosen for classification. The classification results are evaluated on the given set of images of alloy steel surface and the performance of the system is evaluated.
Suboptimal tracking control of nonlinear systems via state-dependent differential Riccati equation for robotic manipulators

This work investigates state-dependent differential Riccati equation (SDDRE) controller for tracking problem. The system in question is a class of time-varying nonlinear systems with control nonlinearities. Regulation case needs only the solution to the SDDRE to provide suboptimal gain of control law, though derivation of tracking problem generates a feedforward differential equation in addition to the SDDRE. In that case, the SDDRE stabilizes the system towards equilibrium point and feedforward equation keeps the output following desired trajectory. The new structure imposes complexity on analysis of stability and solution method, which are addressed in this work. Backward integration (BI) technique is selected to solve the governing equations to obtain suboptimal and feedforward control gains. The application of the proposed controller is applied to robotic field. Trajectory tracking is one of the critical issues in manipulator control area; hence this work is dedicated to it. The BI method released an outstanding precise tracking performance based on its two-round solution in comparison with conventional state dependent Riccati equation (SDRE) controller.
Adaptive sugar sensors in hypothalamic feeding circuits

Brain glucose sensing is critical for healthy energy balance, but how appropriate neurocircuits encode both small changes and large background values of glucose levels is unknown. Here, we report several features of hypothalamic orexin neurons, cells essential for normal wakefulness and feeding: (i) A distinct group of orexin neurons exhibits only transient inhibitory responses to sustained rises in sugar levels; (ii) this sensing strategy involves time-dependent recovery from inhibition via adaptive closure of leak-like K+ channels; (iii) combining transient and sustained glucosensing allows orexin cell firing to maintain sensitivity to small fluctuations in glucose levels while simultaneously encoding a large range of baseline glucose concentrations. These data provide insights into how vital behavioral orchestrators sense key features of the internal environment while sustaining a basic activity tone required for the stability of consciousness.
Faking Errors to Avoid Making Errors: Very Weakly Supervised Learning for Error Detection in Writing

This paper describes a method to create a grammar checker “for free”. It requires no manual work, only unannotated text and a few basic NLP tools. The method used is to simply annotate a lot of errors in written text and train an off-the-shelf machine learning implementation to recognize such errors. To avoid manual annotation artificially created errors are used for training. Recall is comparable to other grammar checkers but precision is lower. Our method also complements traditional grammar checkers, i.e. they do not always find the same errors. The evaluation is performed on real er-
Physiological steady state of phytoplankton in the field? An example based on pigment profile of Emiliania huxleyi (Haptophyta) during a light shift

A calcifying strain of Emiliania huxleyi was used to study the photoacclimation process during a shift from low (LL) to high (HL) photon flux density (PFD) under nutrient‐replete and pH‐ and [CO2]‐controlled continuous cultures. Physiological steady states were obtained after culturing the alga in each PFD for more than a month, and pigment profiles and cell volume changes were monitored for 25 d after the light shift. Fucoxanthin was the major carotenoid in LL, while under HL this role was assumed by 19'hexanoyloxyfucoxanthin (19Hex). The photoprotective pigments diadinoxanthin and diatoxanthin (Dd+Dt), normalized to chlorophyll a (Chl a), increased with increasing PFD, while Chl a content per cell and Chl c's and fucoxanthin, normalized to Chl a, decreased with increasing PFD. The sum of all carotenoids normalized to Chl a and the 19Hex+Fuco : Chl c ratio were remarkably constant from LL to HL conditions. The results confirm that the total amount of carotenoids was synthesized/catabolized in tandem with Chl a to a genetically predefined level independent of PFD. When normalized to a per cell basis, Chl a content reached the long‐term HL steady state after 17‐20 d, while Chl c, Fuco, and Dd+Dt, normalized to Chl a, reached the long‐term HL steady state after 5‐7 d. Growth rate adjustment was completed within 3 d after the transition to HL. When Chl a was normalized to cellular volume, the transition to a fully acclimated HL state was complete within 3 d. The results highlight the need for the critical evaluation of the normalization "currency" (i.e., cell number, volume, Chl a) to which mass is expressed during the photoacclimation process and suggest that natural phytoplankton populations are unlikely to ever be in true physiological steady state.
Evolution of bilateral symmetry in agents controlled by spiking neural networks

We present in this paper three novel developmental models allowing information to be encoded in space and time, using spiking neurons placed on a 2D substrate. In two of these models, we introduce neural development that can use bilateral symmetry. We show that these models can create neural controllers for agents evolved to perform chemotaxis. Neural bilateral symmetry can be evolved and be beneficial for an agent. This work is the first, as far as we know, to present developmental models where spiking neurons are generated in space and where bilateral symmetry can be evolved and proved to be beneficial in this context.
Support Vector Clustering Applied to Digital Communications

Support vector clustering (SVC) is a recently proposed clustering methodology with promising performance for high-dimensional and noisy datasets, and for clusters with arbitrary shape. This work addresses the application of SVC, a kernel-based method, in a context in which the channel equalization problem is conceived as a clustering task. The main challenge, in this case, is to perform unsupervised clustering aiming at the design of an optimal Bayesian or a blind prediction-based receiver without resorting to a priori information about the transmission medium. The proposed technique employs a two-stage procedure -a combination between the use of SVC to obtain a first set of clusters and an auxiliary heuristic to help separating eventual multiple clouds contained in a single cluster and attribute centers to them via an iterated local search (ILS) algorithm. The obtained results indicate that kernel methods can be successfully applied to the field of signal processing.
Developing eHealth Technology for People With Dementia: Towards a Supportive Decision Tool Facilitating Shared Decision-Making in Dementia

People with dementia are confronted with many decisions. However, they are often not involved in the process of the decision-making. Shared Decision-Making (SDM) enables involvement of persons with dementia in the decision-making process. In our study, we develop a supportive IT application aiming to facilitate the decision- making process in care networks of people with dementia. A key feature in the development of this SDM tool is the participation of all network members during the design and development process, including the person with dementia. In this paper, we give insight into the first phases of this design and development process in which we conducted extensive user studies and translated wishes and needs of network members into user requirements.
‘All the lonely people’: embracing autoethnographic creative analytical practice at the 2017 World Masters Games.

ABSTRACT It started on Friday 21st April 2017. That was the opening day of a multi-sport event that involved 24,905 participants, 1,094 non-playing officials and 3,216 volunteers. It was a day that I had been waiting for since March 2012. It was a day I spent largely by myself, surrounded by likeminded active participants and hobbyists from all over 100 different countries. This piece of autoethnographic creative analytical practice (CAP applies Clawson, M. and J. Knetsch 1966. Economics of Outdoor Recreation. Baltimore: John Hopkins) five phases of the leisure experience to the author’s first twelve hours as an athlete at the 2017 World Masters Games. By bringing together my two biggest passions, amateur sport and popular music, I seek to stir up your sociological imagination, and let you see things from my unique perspective. I want you to finish this story, thinking about all the lonely people that you may have unintentionally missed/excluded from your leisure studies research.