ppxscal/embedding_network huggingface dataset:
XTR +: A Provable Security Public Key Cryptosystem
Authors: Zehui Wang, Zhiguo Zhang
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

The XTR is a very effective public key cryptosystem based on 3rd order LFSR sequence. But it has parameter corresponding problem and it neglects the Provable Security property and the blind signature scheme. For overcoming these problems in this paper, the XTR is extended with 4-th order LFSR sequence to from a new public key cryptosystem called XTR + . An algorithm for computing the trace elements is proposed, which only depends on a 2×2 recursive matrix instead of 4×4 so that the running time of the algorithm is much shorter than the algorithm for XTR which depends on a 3×3 recursive matrix.Over XTR + the provable IND-CCA2 secure encryption/decryption protocol, the provable secure digital signature, the provable secure blind signature protocol and zero-knowledge proof protocol are established. Compared with the traditional methods such as ECC, XTR + is more simple in cipherkey and parameter selections and has more randomcity and faster algorithms. Under the same security requirements, the XTR + can greatly reduce the overheads in parameter storage and communication and be suitable for bigger plaintext and ciphertext spaces.
Supply chain models for web-based retailing and rental business
Authors: Apurva Jain, Kamran Moinzadeh
Venue:  (2008)

In this research, we analyze the issues related to Web-based retailing and rental businesses. On the supply side, the possibility of "direct delivery" has given manufacturers access to a new channel to serve customers in addition to the traditional retail channel. On the demand side, since customers "order from home," the sellers find it much easier to collect data about the purchasing patterns of specific customers and then use this data to differentiate between customers. We analyze both these issues. On the supply side, we study a dual channel supply chain in which a manufacturer sells to a retailer as well as to consumers directly. Consumers choose the purchase channel based on price and service qualities. The manufacturer decides the price of the direct channel and the retailer decides both price and order quantity. We develop conditions under which the manufacturer and the retailer share the market in equilibrium. We show that the difference in marginal casts of the two channels plays an important role in determining the existence of dual channels in equilibrium. We show that demand variability has a major influence on the equilibrium prices and on the manufacturer's motivation for opening a direct channel. We also study a centralized version of the model and find conditions under which a dual channel solution is feasible. We propose a contract under which the decentralized solution, under symmetric information, will achieve the performance of centralized solution. On the demand side, motivated by Netflix, we develop a model of the rental process at a subscription-based business. The model captures the main features of the business situation: demand for a title decreases over time, unsatisfied customers return, and there are two classes of customers, light-renters and heavy-renters. Adopting a simple priority rule, we propose an algorithm to search for optimal initial inventory to balance purchasing costs and backlog costs. Also, with the objective to minimize the backlog cost given an initial inventory decision, the analysis leads to the optimal allocation policy. We compare it to a priority scheme used by Netflix, called throttling. Computational results also show the value of preference lists.
Designing a reliable and stable link quality metric for wireless sensor networks
Authors: Michele Rondinone, Junaid Ansari, Janne Riihijärvi, Petri Mähönen
Venue: Proceedings of the workshop on Real-world wireless sensor networks (2008)

We study the problem of link quality (LQ) estimation in wireless sensor networks. Based on an extensive measurement campaign we have investigated in detail the behaviour of usual LQ estimators, such as the Packet Reception Rate (PRR), the received signal strength indicator (RSSI) and the LQI index of the CC2420 radio. All of these turn out to have shortcomings, calling for the design of a new LQ metric. We propose such a metric combining PRR and RSSI in a normalized manner. We show that for a wide variety of link conditions our metric provides stable and accurate link quality estimates. This work has a large number of potential application areas, including more accurate link-aware routing, topology control and different cross-layer optimization schemes.
Epiflow-A paradigm for tracking stereo correspondences
Authors: Hongsheng Zhang, Shahriar Negahdaripour
Venue: Computer Vision and Image Understanding (2008)

We propose the epiflow as a novel framework to integrate the motion and stereo epipolar geometries for the tracking of stereo matches. For a given stereo pair, spatio-temporal correspondences in the next stereo pair are established from the solution of an optimization problem that incorporates the epiflow geometrical constraint with the brightness constancy assumption as the photometric similarity measure. Two different implementations are proposed, each utilizing the stereo RANSAC algorithm as a mechanism to gain robustness and efficiency in handling large temporal disparity ranges, identify outliers and estimate the motion of the stereo rig as complementary information. The performance of the proposed methods is demonstrated in experiments with various complex underwater images, and comparison with results from the SIFT method allows the assessment of our novel contributions.
Simple Generalized Group-Oriented Cryptosystems Using ElGamal Cryptosystem
Authors: Chou-Chen Yang Ting-Yi Chang Jian-Wei Li, Min-Shiang Hwang
Venue: Informatica (2003)

In the generalized group-oriented cryptosystem, the sender can send a conditional message to a group of users such that only the specified sets of users in this group can cooperate to decrypt this message. In this paper, we will use an ElGamal cryptosystem and an elliptic curve ElGamal cryptosystem to achieve the purposes of generalization and group-orientation, respectively. Both of our schemes are more efficient than Tsai et al.'s scheme in terms of sender's computational complexity.
Rule-Based Modelling, Symmetries, Refinements
Authors: Vincent Danos, Jérôme Feret, Walter Fontana, Russell Harmer, Jean Krivine
Venue: Proceedings of the 1st international workshop on Formal Methods in Systems Biology (2008)

Rule-based modelling is particularly effective for handling the highly combinatorial aspects of cellular signalling. The dynamics is described in terms of interactions between partial complexes, and the ability to write rules with such partial complexes -i.e., not to have to specify all the traits of the entitities partaking in a reaction but just those that matter- is the key to obtaining compact descriptions of what otherwise could be nearly infinite dimensional dynamical systems. This also makes these descriptions easier to read, write and modify.In the course of modelling a particular signalling system it will often happen that more traits matter in a given interaction than previously thought, and one will need to strengthen the conditions under which that interaction may happen. This is a process that we call rule refinement and which we set out in this paper to study. Specifically we present a method to refine rule sets in a way that preserves the implied stochastic semantics.This stochastic semantics is dictated by the number of different ways in which a given rule can be applied to a system (obeying the mass action principle). The refinement formula we obtain explains how to refine rules and which choice of refined rates will lead to a neutral refinement, i.e., one that has the same global activity as the original rule had (and therefore leaves the dynamics unchanged). It has a pleasing mathematical simplicity, and is reusable with little modification across many variants of stochastic graph rewriting. A particular case of the above is the derivation of a maximal refinement which is equivalent to a (possibly infinite) Petri net and can be useful to get a quick approximation of the dynamics and to calibrate models. As we show with examples, refinement is also useful to understand how different subpopulations contribute to the activity of a rule, and to modulate differentially their impact on that activity.
Regression and Performance Testing of an e-Learning Web Application: dotLRN
Authors: Ana Cavalli, Stephane Maag, Gerardo Morales
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Along with the continuous development of big and complex Web applications such as dotLRN, it is mandatory to execute in a scheduled way a set of test cases to assure its functional stability and to make sure that the web application still runs whatever the modifications applied to the implementation. This article highlights the importance of testing and presents the types of test that are needed to assure not just the stability but also the scalability of the dotLRN platform by testing some non-functional aspects. This article presents different methods that can be applied to test in general any Web application. Then a methodology to manually develop the test conformance in order to automatically test the regression of the implementation is proposed. Finally this paper presents our proposal to cover the performance and load tests of the dotLRN web application.
A Semi-supervised Clustering Algorithm Based on Must-Link Set
Authors: Haichao Huang, Yong Cheng, Ruilian Zhao
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Clustering analysis is traditionally considered as an unsupervised learning process. In most cases, people usually have some prior or background knowledge before they perform the clustering. How to use the prior or background knowledge to imporve the cluster quality and promote the efficiency of clustering data has become a hot research topic in recent years. The Must-Link and Cannot-Link constraints between instances are common prior knowledge in many real applications. This paper presents the concept of Must-Link Set and designs a new semi-supervised clustering algorithm MLC-KMeans using Musk-Link Set as assistant centroid. The preliminary experiment on several UCI datasets confirms the effectiveness and efficiency of the algorithm.
Lexical and Semantic Resources for NLP: From Words to Meanings
Authors: Anna Lisa Gentile, Pierpaolo Basile, Leo Iaquinta, Giovanni Semeraro
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

A user expresses her information need through words with a precise meaning, but from the machine point of view this meaning does not come with the word. A further step is needful to automatically associate it to the words. Techniques that process human language are required and also linguistic and semantic knowledge, stored within distinct and heterogeneous resources, which play an important role during all Natural Language Processing (NLP) steps. Resources management is a challenging problem, together with the correct association between URIs coming from the resources and meanings of the words.This work presents a service that, given a lexeme (an abstract unit of morphological analysis in linguistics, which roughly corresponds to a set of words that are different forms of the same word), returns all syntactic and semantic information collected from a list of lexical and semantic resources. The proposed strategy consists in merging data with origin from stable resources, such as WordNet, with data collected dynamically from evolving sources, such as the Web or Wikipedia. That strategy is implemented in a wrapper to a set of popular linguistic resources that provides a single point of access to them, in a transparent way to the user, to accomplish the computational linguistic problem of getting a rich set of linguistic and semantic annotations in a compact way.
Mining Crime Data by Using New Similarity Measure
Authors: Guangzhu Yu, Shihuang Shao, Bing Luo
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Clustering technique was introduced to the field of crime data analysis for finding crime suspects, but compared with the high requirements for public security work, traditional clustering methods used in existing application systems provide a relatively low accuracy. To solve the problem, we proposed a new similarity measure method, i.e., Segmented Multiple Metric Similarity Measure (SMMSM), to improve the accuracy of similarity measure. In our method, attributes are divided into different groups according to their importance to similarity, compensation relationships does not exist among attributes in different groups. The new measure is scalable with dimensionality of data and is both suitable for numeric data and categorical data. Experiments on real data show that our method has higher accuracy than other measures.
A Novel Incremental Algorithm for Frequent Itemsets Mining in Dynamic Datasets
Authors: Raudel Hernández-León, José Hernández-Palancar, J. A. Carrasco-Ochoa, J. Fco. Martínez-Trinidad
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

Frequent Itemsets (FI) Mining is one of the most researched areas of data mining. When some new transactions are appended, deleted or modified in a dataset, updating FI is a nontrivial task since such updates may invalidate existing FI or introduce new ones. In this paper a novel algorithm suitable for FI mining in dynamic datasets named Incremental Compressed Arrays is presented. In the experiments, our algorithm was compared against some algorithms as Eclat, PatriciaMine and FP-growth when new transactions are added or deleted.
Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Data
Authors: Jimeng Sun, Charalampos E. Tsourakakis, Evan Hoke, Christos Faloutsos, Tina Eliassi-Rad
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

Data stream values are often associated with multiple aspects. For example, each value observed at a given time-stamp from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Time-stamp, type and location are the three aspects, which can be modeled using a tensor (high-order array). However, the time aspect is special, with a natural ordering, and with successive time-ticks having usually correlated values. Standard multiway analysis ignores this structure. To capture it, we propose 2 Heads Tensor Analysis (2-heads), which provides a qualitatively different treatment on time. Unlike most existing approaches that use a PCA-like summarization scheme for all aspects, 2-heads treats the time aspect carefully. 2-heads combines the power of classic multilinear analysis (PARAFAC [1], Tucker [5], DTA/STA [3], WTA [2]) with wavelets, leading to a powerful mining tool. Furthermore, 2-heads has several other advantages as well: (a) it can be computed incrementally in a streaming fashion, (b) it has a provable error guarantee and, (c) it achieves significant compression ratio against competitors. Finally, we show experiments on real datasets, and we illustrate how 2-heads reveals interesting trends in the data.This is an extended abstract of an article published in the Data Mining and Knowledge Discovery journal [4].
Proving Termination Using Recursive Path Orders and SAT Solving
Authors: Peter Schneider-Kamp, René Thiemann, Elena Annov, Michael Codish, Jürgen Giesl
Venue: Proceedings of the 6th international symposium on Frontiers of Combining Systems (2007)

We introduce a propositional encoding of the recursive path order with status (RPO). RPO is a combination of a multiset path order and a lexicographic path order which considers permutations of the arguments in the lexicographic comparison. Our encoding allows us to apply SAT solvers in order to determine whether a given term rewrite system is RPO-terminating. Furthermore, to apply RPO within the dependency pair framework, we combined our novel encoding for RPO with an existing encoding for argument filters. We implemented our contributions in the termination prover AProVE. Our experiments show that due to our encoding, combining termination provers with SAT solvers improves the performance of RPO-implementations by orders of magnitude.
Supporting the Elicitation of Requirements Compliant with Regulations
Authors: Motoshi Saeki, Haruhiko Kaiya
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

This paper presents a technique to check the compliance of requirements with regulations while eliciting requirements. In our technique, we semantically represent a regulation with combinations of case frames resulting from Case Grammar technique. We match a newly elicited requirement sentence with the case frames of regulation sentences and then check if the requirements include the obligation acts specified by the matched regulation sentences and if they do not have prohibited acts. If we find that a requirement sentence does not follow the regulation, the addition or removal of the illegal acts included in the requirements are suggested.
HMM-Based Action Recognition Using Contour Histograms
Authors: M. Ángeles Mendoza, Nicolás Pérez De La Blanca
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

This paper describes an experimental study about a robust contour feature (shape-context) for using in action recognition based on continuous hidden Markov models (HMM). We ran different experimental setting using the KTH's database of actions. The image contours are extracted using a standard algorithm. The shape-context feature vector is build from of histogram of a set ofnon-overlapping regions in the image. We show that the combined use of HMM and this feature gives equivalent o better results, in term of action detection, that current approaches in the literature.
An Approach to Visual Scenes Matching with Curvilinear Regions
Authors: J. M. Pérez-Lorenzo, A. Bandera, P. Reche-López, R. Marfil, R. Vázquez-Martín
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

This paper presents a biologically-inspired artificial vision system. The goal of the proposed vision system is to correctly match regions among several images to obtain scenes matching. Based on works that consider that humans perceive visual objects divided in its cons-tituent parts, we assume that a particular type of regions, called curvilinear regions, can be easily detected in digital images. These features are more complex than the basic features that human vision uses in the very first steps in the visual process. We assume that the curvilinear regions can be compared in their complexity to those features analysed by the IT cortex for achieving objects recognition. The approach of our system is similar to other existing methods that also use intermediate complexity features for achieving visual matching. The novelty of our system is the curvilinear features that we use.
Affective Adaptation of Synthetic Social Behaviour
Authors: Pablo Lucas Anjos, Ruth Aylett, Alison Cawsey
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

This research focuses on designing affective roles in agent-based social simulation (ABSS) focused on ethology. Synthetic agents are addressed as autonomous, intentional software entities capable of managing primate-like (hierarchical) social relationships in small-scale societies. The critique involves discussion of potential affective roles in socio-cognitive agent architectures, both in terms of individual action-selection and group organisation. With the diversity of social and emotional accounts, primate-like ABSS is put forward with individual behaviour related not only to reactivity or focused on function-optimisation.
Adaptive Control for a Class of Nonlinear Time-Delay Systems Using RBF Neural Networks
Authors: Geng Ji, Qi Luo
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, adaptive neural network control is proposed for a class of strict-feedback nonlinear time-delay systems. Unknown smooth function vectors and unknown time-delay functions are approximated by two neural networks, respectively, such that the requirement on the unknown time-delay functions is relaxed. In addition, the proposed systematic backstepping design method has been proven to be able to guarantee semiglobally uniformly ultimately bounded of closed loop signals, and the output of the system has been proven to converge to a small neighborhood of the desired trajectory. Finally, simulation result is presented to demonstrate the effectiveness of the approach.
Brief paper: Root-mean-square gains of switched linear systems: A variational approach
Authors: Michael Margaliot, João P. Hespanha
Venue: Automatica (Journal of IFAC) (2008)

We consider the problem of computing the root-mean-square (RMS) gain of switched linear systems. We develop a new approach which is based on an attempt to characterize the ''worst-case'' switching law (WCSL), that is, the switching law that yields the maximal possible gain. Our main result provides a sufficient condition guaranteeing that the WCSL can be characterized explicitly using the differential Riccati equations (DREs) corresponding to the linear subsystems. This condition automatically holds for first-order SISO systems, so we obtain a complete solution to the RMS gain problem in this case.
A Validation Framework for Formal Models in Adaptive Work-Integrated Learning
Authors: Barbara Kump
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

The focus of my thesis is on the development of a multi-method framework for the validation of formal models (domain model, user model, and teaching model) for adaptive work-integrated learning. In order to test its general applicability, the framework will be applied in four different realistic work domains. In this article, specific challenges of traditional validating approaches in work-integrated learning are being discussed. Eventually, the core ideas and methods of the validation framework are outlined.