waashk/dblp huggingface dataset:
estimating human pose from occluded images. computed prediction represents a major shift in learning classifier system research. xcs with computed prediction, based on linear approximates, has been applied so far to function approximation, to single step problems involving continuous payoff functions, and to multi step problems. in this paper we take this new approach in a different direction and apply it to the learning of boolean functions - a domain characterized by highly discontinuous 0/1000 payoff functions. we also extend it to the case of computed prediction based on functions, borrowed from neural networks, that may be more suitable for 0/1000 payoff problems: the perceptron and the sigmoid. the results we present show that xcsf with linear prediction performs optimally in typical boolean domains and it allows more compact solutions evolving classifiers that are more general compared with xcs. in addition, perceptron based and sigmoid based prediction can converge slightly faster than linear prediction while producing slightly more compact solutions
temporal-spatial local gaussian process experts for human pose estimation. in this paper, we analyze the system and channel characteristic based on multi-band orthogonal frequency division multiplexing (mb-ofdm) ultra wide-band (uwb), and propose the corresponding improved channel estimation and tracking algorithms. the proposed channel estimation algorithm which is based on the improved dft algorithm is quite suitable for the low snr environment. it conditionally cuts the paths with little energy within the window, then decreases the infection caused by noise. the proposed channel tracking algorithm aimed at the considerable performance damage coursed by frequency offset conquers the sparseness of pilots, tracks and corrects the amplitude attenuation and phase rotation coursed by frequency offset. the simulation between improved and traditional algorithms is carried out and the results show that better performance can be achieved.
robust focal length estimation by voting in multi-view scene reconstruction. collaborative design and application integration emphasize the demand for database security. database encryption is widely adopted to ensure data privacy, which can prevent attacks from both outside intruders and inside malicious users. current researches on this area are mainly focusing on encryption algorithms, key management and encryption efficiency. however, data sharing nature of database system is usually neglected. in this paper, we propose an access control model, 3s-rbac, which enables data sharing while guaranteeing privacy. the model has many features: the novel concept of strong permission and weak permission; the hierarchy of database objects and keys; the permission and key inheritance; the binding of keys and permissions. implementation in oscar secure dbms shows that the model is flexible, secure, practical, and can be integrated easily into existing enterprise applications
manifold estimation in view-based feature space for face synthesis across poses. in this paper, we propose a novel spatial autocorrelation model of the shadow fading process in urban macro environments. the proposed model is based on the empirical results obtained from extensive wideband radio channel measurement campaigns at 2.35 ghz in an urban area of a typical medium-sized chinese city. the shadow fading component was extracted assuming a single-slope log-distance path loss model. the consistency with the level crossing theory of gaussian processes is achieved by an implicit constraint on the parameters of the model. the proposed model gives a better fit to the empirical results in individual measurement routes than the widely reported exponential and double exponential models. an heuristic explanation of the proposed autocorrelation property is also presented.
learning group activity in soccer videos from local motion. hybrid wireless-optical access network (woan) is a newly emerged network which integrates passive optical networks (pons) and wireless mesh neworks(wmns) to provide the ubiquitous, high bandwidth last mile internet access. in woan, transmission delay for traffic up to the internet consists of wireless path delay in wmns and waiting delay at onus due to the tdma schedule. the onu from which transmission delay is minimal is chosen to be the destination for traffic and the minimal transmission delay is defined as delay bound. in the paper, we intend to find the wireless path to the destinated onu with path delay no more than delay bound and meanwhile with the minimal maximal link delay, which is defined as interference aware and delay bounded routing (iadbr) problem. we propose both centralized and distributed algorithms to solve iadbr. simulation results show that the distributed algorithm performs quite closely to the optimal centralized algorithm and performs much better than the shortest path algorithm.
clustering-based descriptors for fingerprint indexing and fast retrieval. a high fault detection coverage is very critical for systems with ultra-safe requirements and fault injection is an effective technique for estimating the coverage. one difficulty of fault injection lies in the huge number of injections that need to be carried out in order to obtain statistically significant results. fault expansion has been proposed as a means of reducing the number of injections by dividing faults into equivalence classes. we show that this intuitively appealing approach is only effective when each fault equivalence class is a significant portion of the fault population
level set segmentation based on local gaussian distribution fitting. we present an algebraic approach to multibody motion segmentation from line correspondences. given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody line constraint of translational motions. we show how to linearly estimate the multibody trifocal epipole from line-line-line correspondences. the individual trifocal epipoles are then obtained from the derivatives of the multibody line constraint (up to an unknown factor). given normalized trifocal epipoles, we can use any special clustering technique to obtain the clustering of the motions and the correspondences. the limitations of the proposed algorithm are also discussed. experimental results on synthetic and real dynamic scenes are presented.
natural image segmentation with adaptive texture and boundary encoding. this paper proposes the ssoa (semantic service-oriented architecture) based on formal and distributed ontologies, and the knowledge base framed by ontology and the fuzzy reasoning system oriented to agent cognition are considered to prescribe and treat the uncertainty of agent cognizing. the ssoa includes two essential components: knowledge base supported by formal ontology and agent-oriented fuzzy reasoning system. first, knowledge base framed by formal ontology builds a concept hierarchy organized by inclusion relation, and uses those concepts to describe specific objects to form assertions of an application domain. agent-oriented fuzzy reasoning system then incorporates plausibility degree into assertions of certain agent, and the alphabet of symbols, well-formed formulas, axioms and inferences rules are constructed respectively. the ssoa architecture constructs a fundamental for services interaction and composition automatically under open environment.
human action recognition under log-euclidean riemannian metric. location information should be verifiable in order to support new computing and information services. in this paper, we adapt the classical challenge-response method for authentication to the task of verifying an entity's location. our scheme utilizes a collection of transmitters, and adapts their power allocations to verify a user's claimed location. this strategy, which we call power-modulated challenge response, is able to be used with existing wireless sensor networks, and we present three variations. first, we propose a direct method, where some transmitters are selected to send "challenges" that the claimant node should be able to witness based on its claimed location, and for which the claimant node must correctly respond in order to prove its location. second, we reverse the strategy by presenting an indirect method, where some transmitters send challenges that the claimant node should not be able to witness. finally, we present a signal strength based method, where the node responds with its received signal strength and thereby provides improved location verification. to evaluate our schemes, we examine different adversarial models for the claimant, and characterize the performance of our power-modulated challenge response schemes under these adversarial models.
a novel hierarchical model of attention: maximizing information acquisition. in this paper, we proposed an automatic method to segment text from complex background for recognition task. first, a rule-based sampling method is proposed to get portion of the text pixels. then, the sampled pixels are used for training gaussian mixture models of intensity and hue components in hsi color space. finally, the trained gmms together with the spatial connectivity information are used for segment all of text pixels form their background. we used the word recognition rate to evaluate the segmentation result. experiments results show that the proposed algorithm can work fully automatically and performs much better than the traditional methods.
categorization of multiple objects in a scene without semantic segmentation. compositional modelling (cm) has been applied to synthesize automatically plausible scenarios in many problem domains with promising results. however, it is assumed that the generic and reusable model fragments within the knowledge base can all be expressed by precise and crisp information. this paper presents an initial attempt to extend the existing cm work to allow the generation of scenario spaces which are capable of representing, storing and supporting inference about imprecise or ill-defined data, by the use of fuzzy sets. a knowledge representation formalism for both fuzzy parameters and fuzzy constraints is incorporated into the representation of conventional model fragments. the applicability of the proposed method is illustrated by means of a simple worked example for supporting crime investigation.
image enhancement of low-light scenes with near-infrared flash images. we propose a practical and secure electronic voting protocol for large-scale online elections. our protocol satisfies a large set of important criteria that has never been put together in a single protocol before. among all electronic voting schemes in the literature, sensus, a security-conscious electronic voting protocol proposed by cranor and cytron (1997), satisfies most of our criteria. sensus has been implemented and used in mock elections. however, sensus suffers from several major drawbacks. for instance, we show that even if all voters follow the sensus protocol honestly, some voters' votes may still be replaced with different votes without being detected. our protocol overcomes these drawbacks
efficient partial shape matching of outer contours. we present netspy, a tool to automatically generate network-level signatures for spyware. netspy determines whether an untrusted program is spyware by correlating user input with network traffic generated by the untrusted program. if classified as spyware, netspy also generates a signature characterizing the malicious substrate of the spyware's network behavior. such a signature can be used by network intrusion detection systems to detect spyware installations in large networks. in our experiments, netspy precisely identified each of the 7 spyware programs that we considered and generated network-level signatures for them. of the 9 supposedly-benign programs that we considered, netspy correctly characterized 6 of them as benign. the remaining 3 programs showed network behavior that was highly suggestive of spying activity
3d motion segmentation using intensity trajectory. in this paper, we carry out a study on the rns (residue number system) application in digital image processing and propose a rns image coding scheme that offers high-speed and low-power vlsi implementation for secure image processing. the proposed scheme is more efficient than the rns image coding scheme of ammar et al. (2001) in that the proposed method encrypts the entire image and does not require any additional component other than a standard rns system. further, the proposed scheme is based on the modified crt and its associated residue-to-binary conversion and moduli selection methods and is more efficient than the scheme by ammar et al. (2001) in terms of vlsi implementation. the design of an encoder and decoder pair for the greyscale image is carried out using matlab tool and some vlsi tools. the preliminary results of the matlab simulation demonstrate the security ability of the proposed image coding scheme.
mean-shift object tracking with a novel back-projection calculation method. multicore architectures, which have multiple processing units on a single chip, are widely viewed as a way to achieve higher processor performance, given that thermal and power problems impose limits on the performance of single-core designs. accordingly, several chip manufacturers have already released, or will soon release, chips with dual cores, and it is predicted that chips with up to 32 cores will be available within a decade. to effectively use the available processing resources on multicore platforms, software designs should avoid co-executing applications or threads that can worsen the performance of shared caches, if not thrash them. while cache-aware scheduling techniques for such platforms have been proposed for throughput-oriented applications, to the best of our knowledge, no such work has targeted real-time applications. in this paper, we propose and evaluate a cache-aware pfair-based scheduling scheme for real-time tasks on multicore platforms
combining discriminative and descriptive models for tracking. we introduce a social network analysis method as a new approach to build an intrusion detection system (sn-ids) in ad hoc networks. the sn-ids utilizes social relations as metrics-of-interest for anomaly detections, which is different from most traditional ids approaches. to construct proper social networks, we first investigate ad hoc mac and network layer data attributes and select relevant social feature sets; then we build up a set of socio-matrices based on these features. social analysis methods are applied to these matrices to detect suspicious behaviors of mobile nodes. ns-2 simulation results show that this sn-ids system can effectively detect common attacks with high detection rates and low false positive alarm rates. furthermore, it has clear advantages over the conventional association rule based data mining ids in terms of computation and system complexity.
3d reconstruction of human motion and skeleton from uncalibrated monocular video. soft-decision decoding of a linear block code using the most reliable basis corresponding to each received word is investigated. based either on probabilistic properties or on the structure of the code considered, three improvements to the algorithm devised by fossorier and lin (see ibid., vol.41, no.9, p.1379-1396, 1995) are presented. these modifications allow large computation savings or significant decoding speedup with little error performance degradation. first, a reduced probabilistic list of codeword candidates is associated with order-i reprocessing of a given code. it results in a large reduction of the maximum number of computations with a very small degradation in performance. then, a probabilistic stopping criterion is introduced for order-0 reprocessing. this new test significantly decreases the average number of computations when appropriately implemented. finally, the application of the algorithm to coset decoding is considered for |u|u+v| constructed codes. in addition to the conventional coset decoding, a new adaptive practically optimum coset decoding method is presented where at each reprocessing stage, the number of surviving cosets decreases. suboptimum closest coset decoding is also investigated. it is shown that two-stage decoding with the algorithm of fossorier and lin offers a large variety of choices, since the reprocessing order of each stage can be determined independently
multiple view reconstruction of a quadric of revolution from its occluding contours. support vector machine (svm) is a new sort of machine learning method based on structure risk minimization (srm) principle, which has high generalization capability. many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. in this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on svm about freeway incident detection was proposed. a svm was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. compared with the multi-layer feed forward neural network (mlf) algorithm trained with the same data, the simulation results showed that the svm offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance
interactive shadow removal from a single image using hierarchical graph cut. we examine the coding problem of base layer (bl) in fine granularity scalable videos and rate adaptation of enhancement layer (el) during streaming in order to support constant quality streaming. the problem arises from the facts that different frames of bl often exhibit significant quality variation in the usual fgs bl encoding methods, and consequently the actual el rate-distortion (r-d) curves of different frames present significant difference. under this condition it would be computationally exhaustive to scale the el to flatten out the fluctuating bl quality and to attain constant quality. we propose, in this paper, an accurate constant quality encoding method of bl, under which we investigate the similarity of actual el r-d curves, and then a simple but effective weight-based el rate allocation algorithm is introduced. experiments show, compared with default methods, our fast and simple methods not only provide constant-quality streaming, but also decrease the average mse of videos.
a shape derivative based approach for crowd flow segmentation. more and more softwares based on web service technologies are developed. before their releases on the internet, it is necessary to evaluate these systems' performance, especially their response time under different workload pressures. however, existing performance testing benchmarks and tools for web service applications are difficult to adapt to various user-specific testing purposes. this paper proposes a configurable web service performance testing framework which contains client module, application server module and database module. client module, by using the network cooperation method that one central client drives several other clients, adapts to a great number of concurrent customers to request web services. application server module contains web services under testing and external supporting web services, each of which is configured as a plug-in. the process to realize mixed ratio of web service interactions is similar to dealing cards and adapts to different commercial application characteristics. in database module, the data model including table and attribute dependence can be customized, and the data scale initialization can be resized according to the topology of above dependence. as such, this framework allows testers to dynamically define their data model, customize their scale of database, configure their transaction characteristics, deploy their application strategies and confirm their performance metrics..
incorporating spatial correlogram into bag-of-features model for scene categorization. a reconfigurable modular snake robot has been developed, which can not only move on a plane but also achieve some 3-dimensional motions while reconfigured. control equations of 3-dimensional locomotion were established by the composition of two bending motions in mutual orthogonal plane. three types of lateral rolling locomotion, flapping, linear rolling and curved rolling, were achieved by controlling the amplitudes and the number of two waves in the two bending motions. using the three types of locomotion the snake robot can realize net lateral translation, alternation of its contact base and rolling over some obstacles. the lateral rolling locomotion obtains its driving force through the interaction with the environment the rolling shape and its direction depend on the transferring direction and phase difference of the two waves respectively.
towards robust object detection: integrated background modeling based on spatio-temporal features. we present a new algorithm for the automatic recovery of tag grid-line intersections in tagged mr images of the left ventricle of the heart. our method uses an active spring mesh to capture local properties of the motion and a global motion model to capture the global coherence of the motion. we recover the global component of the motion using robust estimation. different motion models have been developed for short and long axis views of the heart. the algorithm has been tested on healthy and pathological data
vehicle headlights detection using markov random fields. sign language is the primary modality of communication among deaf and mute society all over the world. this paper proposes a viewpoint independent method for sign recognition. considering that two sequences of the same sign can be roughly considered as the input of a stereo vision system after time-warping, and the fundamental matrix associated with two views should be unique, we can convert the temporal-spatial recognition task as a verification task within a stereo vision framework. after time-warping of the input sequences, the proposed framework can reach both temporal and viewpoint invariance. we demonstrate the efficiency of the proposed framework by recognizing a vocabulary of 100 words of chinese sign language. the recognition rate is up to 97% at rank 3. furthermore, the proposed framework can be easily extended to other recognition tasks, such as gait recognition and lip-reading recognition.
multilevel algebraic invariants extraction by incremental fitting scheme. this paper give the applications of safety audit and monitor technology in power management system. this paper first introduce the audit technology and its working environment and then introduce the elopements of the system which including the design of monitoring module in proxy and network capability, etc. and it also proposes the implement flow of these function models including the implement flow of security audit model and network monitoring model, it also gives the design and realization of ip address peculate model and monitoring module in proxy.