Miracle-dz/UniFAR-FTUs huggingface dataset:
Forbidden Nodes Aware Community Search

Community search is an important problem in network analysis, which has attracted much attention in recent years. It starts with some given nodes, pays more attention to local network structures, and gets personalized resultant communities quickly. In this paper, we argue that there are many real scenarios where some nodes are not allowed to appear in the community. Then, we introduce a new concept called forbidden nodes and present a new problem of forbidden nodes aware community search to describe these scenarios.To address the above problem, three methods are proposed, i.e., k-core based FORTE (Forbidden nOdes awaRe communiTy sEarch), k-truss based FORTE and CW based FORTE, where the effects of both forbidden nodes and query nodes are thoroughly considered for each node in the resultant community. The former two methods are able to make use of popular community structures, while the latter is based on a new metric called weighted conductance. The extensive experiments conducted on real data sets demonstrate the effectiveness of the proposed methods.
Query rewriting in spatial database for access control

Current database with numerous users requires access control. It is also needed in spatial database systems that keep both spatial attribute and non-spatial attribute safe. The query rewriting technology in spatial database is discussed in this paper. First the operations for merging authorization policies are discussed. By classifying the subjects and permit flag of the authorization policies, two operations, both intersection and union, are discussed with different purpose. And an algorithm for merging user's authorization policies is prompted. Then a view based query rewriting method is set up by replacing the all relations in the query with the corresponding view. That makes sure the finegrained access control for the spatial database and spatial objects. Finally, a spatial database as an application is given to validate the effectiveness of query rewriting technology in spatial database.
Multilingual lexicons for machine translation

Despite the numerous bad press received by machine translation systems, they are invaluable in aiding users to quickly gather the essence of texts written in an unfamiliar language, particularly on the Web. Sufficiently accurate translation of lexical items is essential to satisfy such needs. Lexicon resources, especially a well-designed multilingual lexicon is needed to facilitate effective translation selection of lexical items, consisting of either single or multiple words. We describe the issues facing multilingual lexicography, and review how they are handled in selected multilingual lexical database projects. This preliminary surveys aims at identifying multilingual issues and concerns in translating lexical items from a machine translation perspective, and will guide the design of a multilingual lexicon intended for use in a machine translation system.
Implementation of Mobile Robot Localisation and Path Planning for Navigation in Known Map

This paper describes the implementation of a laser scanner to localise a mobile robot in a known map and navigate to a pinpointed location while avoiding any obstacles within the path. The ability to successfully localise itself is a key requirement for any mobile robot. The Turtlebot Create is used as the platform part to test the localisation. The probabilistic localisation method, the Adaptive Monte Carlo Localization (AMCL) technique is used for the purpose. The approach is experimentation driven due to unique and unanticipated challenges present in the environment. As for obstacle avoidance and path planning, the Dynamic Window Approach (DWA) and Dijkstra’s algorithm is used respectively. The implementation is done using Robot Operating System (ROS) framework and thus is reusable in any future projects with both, simulated and real platforms. The experiments were run in CEASTech and UniMAP Solutions, and it was observed that the robot was able to firstly map the two environments, localise itself in the known map and manoeuvre to the pinpointed locations without any collision.
Sparse Bayesian Learning for the Time-Varying Massive MIMO Channels: Acquisition and Tracking

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO systems. In this paper, with the help of the virtual channel representation, we apply such property to both time-division duplex and frequency-division duplex systems, where the time-varying channel scenarios are considered. First, we formulate the dynamic massive MIMO channel as one sparse signal model. Then, an expectation maximization-based sparse Bayesian learning framework is developed to learn the model parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch–Tung–Striebel smoother are utilized to track the model parameters of the uplink (UL) spatial sparse channel in the expectation step. During the maximization step, a fixed-point theorem-based algorithm and a low-complex searching method are constructed to recover the temporal varying characteristics and the spatial signatures, respectively. With the angle reciprocity, we recover the downlink (DL) model parameters from the UL ones. After that, the KF with the reduced dimension is adopt to fully exploit the channel temporal correlations to enhance the DL/UL virtual channel tracking accuracy. A monitoring scheme is also designed to detect the change of model parameters and trigger the relearning process. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.
A novel model on dynamic resource allocation in optical networks

A novel model on dynamic resource allocation in the WDM optical networks is proposed, basing on the integrated considerations of the impacts of transmission impairments and service classification on dynamic resource allocation in the optical layer. In this model, the priorities of optical connection requests are mapped into different thresholds of transmission impairments, and a uniform method which is adopted to evaluate the virtual wavelength path (VWP) candidates is defined. The Advanced Preferred Wavelength Sets Algorithm (A-PWS) and the heuristic Dynamic Min-Cost & Optical Virtual Wavelength Path Algorithm (DMC-OVWP) are presented addressing the routing and wavelength assignment (RWA) problem based on dynamic traffic and multi priorities in wavelength-routed optical networks. For a received optical connection request, DMC-OVWP is employed to calculate a list of the VWP candidates, and an appropriate VWP which matches the request’s priority is picked up to establish the lightpath by analyzing the transmission qualities of the VWP candidates.
The Complex Alarming Event Detecting and Disposal Processing Approach for Coal Mine Safety Using Wireless Sensor Network

Due to the complex environment of the coal mine, the accidents can occur at any time and often result in partial or total evacuation of mine personnel and could result in the loss of lives. Therefore, it is important and necessary to detect the accidents and generate a corresponding alarming disposal in time. This paper proposed a real-time complex alarming event detecting and disposal processing approach for coal mine safety using wireless sensor network. Firstly, we introduce the event and complex events model, offer fully customizable policies for event selection and consumption, and also describe the state-automata-based complex event detection algorithm. Then, we describe an event-driven service coordination pattern for the behavioral model, which is based on event-condition-action triggering with BPEL control flow realized using decoupled publish/subscribe semantics. Finally, the system implementation is presented and deployed in the coal mine, showing the effectiveness of the proposed approach.
Evaluation and optimization of method calls in Java

Recent advances in Virtual Machine's technology have led to the diffusion of Java execution environments which provide performance levels comparable to those of traditional languages such as CsCpp and Fortran. This paper's focus is on method calls, a well researched area of compilers' optimization. In particular, we evaluate the impact on the performance of the Java virtual call mechanism and the advantages which can be obtained through the inlining optimization. Moreover, we propose a new heuristic methodology which allows us to consider whole sequences of methods for the inlining decision and not only simple couples of caller–callee methods.The proposed methodology operates at bytecode level and is applicable only to the calls that can be resolved at compile time. Thus, we execute a devirtualization algorithm before the inlining decision, in order to enlarge the basis for the optimization.We made empirical measures on three major platforms lUnix, Windows and Linuxr by using the SPEC JVM98 benchmark suite. Results reveal an average performance improvement of 1.9p with devirtualization and 5.5p with inlining. We discuss the different behaviour of single benchmarks and virtual machines, arguing that dynamic compilers are not yet able to fully exploit the wider scope for intra-procedural optimization offered by eliminating the calls. Finally, we show that taking into consideration whole sequences of calls—instead of single calls—is effective only on a few benchmarks where hotspots of execution involve cycles of nested methods. Copyright © 2003 John Wiley & Sons, Ltd.
Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold

ABSTRACT Semantic segmentation is an important method to implement fine-grained semantically understand for high-resolution remote sensing images by dividing images into pixel groupings which can then be labelled and classified. In the field of computer vision (CV), the methods based on fully convolutional network (FCN) are the hotspot and have achieved state-of-the-art results. Compared with popular datasets in CV such as PASCAL and COCO, class imbalance is a problem for multiclass semantic segmentation in remote sensing datasets. In this paper, an FCN-based model is proposed to implement pixel-wise classifications for remote sensing image in an end-to-end way, and an adaptive threshold algorithm is proposed to adjust the threshold of Jaccard index in each class. Experiments on DSTL dataset show that the proposed method produces accurate classifications in an end-to-end way. Results show that the adaptive threshold algorithm can increase the score of average Jaccard index from 0.614 to 0.636 and achieve better segmentation results.
Scaling Up the Accuracy of K-Nearest-Neighbour Classifiers: A Naive-Bayes Hybrid

Abstract k-nearest-neighbour (KNN) has been widely used as an effective classification model. In this paper, we summarize three main shortcomings confronting KNN and then single out three categories of approaches for overcoming its three main shortcomings. After reviewing some algorithms in each category, we presented a hybrid algorithm called dynamic k-nearest-neighbour naive Bayes with attribute weighting (simply DKNAW) by combining three improved approaches. We conduct extensive empirical comparison for the related algorithms in four groups, using the whole 36 UCI data sets selected by Weka. In the first three groups, we compare some algorithms in each category accordingly. In the forth group, we compare our hybrid approach to each single approach. At last, we discuss some directions for our future work on KNN
SSInSE: an intelligent search engine based on WWW structure analysis

In this paper, characteristics and shortcomings of three generations of web search engines are analyzed, along with users’ search requirements. Based on these observations, we propose a new strategy to build large scale comprehensive search engines: to replace pages with coarser granularity-- semantically common topic page groups--as the basic unit of a search engine. To provide intelligent search service, we will make heavy use of links to reflect human thoughts.
Optimum Nyquist windowing in OFDM receivers

Conventional orthogonal frequency-division multiplexing receivers disregard the guard interval in channels with small dispersion even though its unconsumed portion can be exploited for improved demodulation by applying a Nyquist-shaped window in the receiver. The transmitter does not need to be modified, subcarrier orthogonality in the receiver is preserved, and the receiver discrete Fourier transform size can be retained. Windowing can mitigate the joint effect of additive noise and intercarrier interference among subcarriers caused by the carrier frequency offset. A closed solution for optimum window coefficients suited to this scenario is derived.
Compression-Based Similarity

First we consider pair-wise distances for literal objects consisting of finite binary files. These files are taken to contain all of their meaning, like genomes or books. The distances are based on compression of the objects concerned, normalized, and can be viewed as similarity distances. Second, we consider pair-wise distances between names of objects, like "red" or "christianity. " In this case the distances are based on searches of the Internet. Such a search can be performed by any search engine that returns aggregate page counts. We can extract a code length from the numbers returned, use the same formula as before, and derive a similarity or relative semantics between names for objects. The theory is based on Kolmogorov complexity. We test both similarities extensively experimentally.
Zero-Knowledge Proofs of Knowledge Without Interaction (Extended Abstract)

A zero-knowledge proof system of knowledge is a protocol between two parties called the prover and the verifier. The prover wants to convince the verifier that he “knows” the proof of a given theorem without revealing any additional information. This is different from a zero-knowledge proof system of membership where the prover convinces the verifier only of the veridicity of the statement. Zero-knowledge proofs of knowledge are very useful tools in the design of secure protocols. Though, the concept of a proof of knowledge is a very subtle one and great care is needed to obtain a satisfying formalization. In this paper, we investigate the concept of a zero-knowledge proof of knowledge in the noninteractive model of [5, 61. Here, the prover and the verifier share a short random string and the only communication allowed is from the prover to the verifier. Although this is a simpler model than the interactive one, still formalizing zeroknowledge proofs of knowledge is a delicate task. The main results of the paper are the following e We present formal definitions for the concept of non-interactive zero-knowledge proofs
Incremental modelling and verification of the PCI express transaction layer

PCI Express is a modern, high-performance communication protocol implementing highly sophisticated features to meet today's performance demands. Although an off-chip protocol, PCI Express implements many principles of future on-chip communication architectures. It is a highly complex protocol which is naturally hard to verify formally. We recently proposed a new methodology, based on a series of model transformation steps, to revise the traditional modelling and verification workflow for designing on-chip protocols. We present the application of the new approach to the PCI Express transaction layer. The work has been accomplished in the Isabelle/HOL theorem prover. By restricting the models to an executable subset of the specification language, we have been able to combine the advantages of specifying in a theorem prover with the advantages of executable models in a functional programming language.
Test Data Compression Using Dictionaries with Fixed-Length Indices

We present a dictionary-based test data compressionapproach for reducing test data volume and testing time in SOCs.The proposed method is based on the use of a small number ofATE channels to deliver compressed test patterns from the testerto the chip and to drive a large number of internal scan chainsin the circuit under test. Therefore, it is especially suitable for areduced pin-count and low-cost DFT test environment, where anarrow interface between the tester and the SOC is desirable. Thedictionary-based approach not only reduces testing time but it alsoeliminates the need for additional synchronization and handshakingbetween the SOC and the ATE. The dictionary entries are determinedduring the compression procedure by solving a variantof the well-known clique partitioning problem from graph theory.Experimental results for the ISCAS-89 benchmarks and representativetest data from IBM show that the proposed method outperformsa number of recently-proposed test data compression techniques.
1. Lifelike Computer Characters: the Persona project at Microsoft Research

This article describes the Persona project at Microsoft Research, which is exploring social user interfaces that employ an explicitly anthropomorphic character to interact with the user in a natural spoken dialogue. The prototype system described here integrates spoken language input, a simple conversational dialogue manager, reactive 3D animation, speech output and sound effects to create Peedy the Parrot, a conversational assistant who accepts user requests for audio CDs and then plays them. 1. Lifelike Computer Characters: the Persona project at Microsoft Research ______________________________________________________1 1.
A Hybrid metaheuristic Navigation Algorithm for robot Path rolling Planning in an unknown Environment

In this paper, a new method for robot path rolling planning in a static and unknown environment based on grid modelling is proposed. In an unknown scene, a local navigation optimization path for the robot is generated intelligently by ant colony optimization (ACO) combined with the environment information of robot’s local view and target information. The robot plans a new navigation path dynamically after certain steps along the previous local navigation path, and always moves along the optimized navigation path which is dynamically modified. The robot will move forward to the target point directly along the local optimization path when the target is within the current view of the robot. This method presents a more intelligent sub-goal mapping method comparing to the traditional rolling window approach. Besides, the path that is part of the generated local path based on the ACO between the current position and the next position of the robot is further optimized using particle swarm optimization (PSO), which resulted in a hybrid metaheuristic algorithm that incorporates ACO and PSO. Simulation results show that the robot can reach the target grid along a global optimization path without collision.
A 256-point discrete Fourier transform processor fabricated in a 2 /spl mu/m NMOS technology

The authors describe a 2-/spl mu/m NMOS single-chip processor which computes a 256-point Fourier transform in 6.5 ms by means of the DFT algorithm. This chip is primarily intended for speech processing applications such as voice recognizers, pitch extractors, and DFT vocoders. The DFT algorithm has been implemented wih two parallel multipliers, an accumulator/shifter and two ROMs with separate address computation units. The processor contains over 20,000 transistors and dissipates ~400 mW when operating at a clock frequency of 10 MHz. The die size is only 12.5 mm/SUP 2/ as a result of the use of a full-custom design method and the incorporation of low-resistance implanted As/SUP +/ undercrossings within the logic circuitry.
Profile-Driven Selective Code Compression

In the embedded system design, memory is one of the most restricted resources. Code compression has been proposed as a solution to reduce the code size of applications for embedded systems. Data compression techniques are used to compress programs to reduce memory size. Most previous work compresses all instructions found in an executable, without taking into account the program execution profile. In this paper, a profile-driven code compression design methodology is proposed. Program profiling information can be used to help code compression to selectively compress non-critical instructions, such that the system performance degradation due to the decompression penalty is reduced.
Design Tool of Deep Convolutional Neural Network for Intelligent Visual Inspection

Recently, convolutional neural networks (CNNs) are used essentially to classify images as it helps to cluster them by similarity and perform recognition. In this paper, a design tool that helps to develop different deep CNNs (DCNNs) is presented. As an example, a DCNN is designed by using the developed tool to use it for vision based inspection to recognize undesirable defects such as crack, burr, protrusion and chipping which normally occur in the manufacturing process of resin molded articles. An image generator is implemented to efficiently produce many similar images for training. Similar images are easily generated by rotating, translating, scaling and transforming original images. The designed DCNN is trained by using the produced images and then tested through classification experiments. The usefulness of the design tool and the basic performance of the designed DCNN are introduced.
Nonlinear Curve Fitting Using Extreme Learning Machines and Radial Basis Function Networks

Fast neural network techniques—extreme learning machine and radial basis function networks—are used to solve the traditional curve fitting problems, where the localized effect of a network basis function is corrected by introducing a linear neuron. Numerical experiments demonstrate that the neural-based method is accurate and well-suited for fitting large volume of data.
A Maximum Entropy Approach to HowNet-Based Chinese Word Sense Disambiguation

This paper presents a maximum entropy method for the disambiguation of word senses as defined in HowNet. With the release of this bilingual (Chinese and English) knowledge base in 1999, a corpus of 30,000 words was sense tagged and released in January 2002. Concepts meanings in HowNet are constructed by a closed set of sememes, the smallest meaning units, which can be treated as semantic tags. The maximum entropy model treats semantic tags like parts-of-speech tags and achieves an overall accuracy of 89.39%, outperforming a baseline system, which picks the most frequent sense.
A model of noisy software engineering data (status report)

other projects in the database. Software development data is highly variable, which often result,s in underlying trends being hidden. In order to address this problem, a method of data analysis, adapted from the financial community, is presented that permits the shape of the curve of some activity to be reduced to a few line segments, called the characteristic curve. This process is used on sample data from the NASA/GSFC Software Engineering Laboratory and has shown to be a reasonable method to understand ttie underlying process being plotted. Lines of code produced, staff hours, error reports filed, change reports filed, modules created, and modules changed are all reported weekly by project personnel or are extracted automatically from the source code libraries. Once in the data base, we can plot these as raw data weekly scatter plots or as various growth functions to show how the collected statistics change over time.