Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated testing for SQL injection vulnerabilities: an input mutation approach

Web services are increasingly adopted in various domains, from finance and e-government to social media. As they are built on top of the web technologies, they suffer also an unprecedented amount of attacks and exploitations like the Web. Among the attacks, those that target SQL injection vulnerabilities have consistently been top-ranked for the last years. Testing to detect such vulnerabilities before making web services public is crucial. We present in this paper an automated testing approach, namely μ4SQLi, and its underpinning set of mutation operators. μ4SQLi can produce effective inputs that lead to executable and harmful SQL statements. Executability is key as otherwise no injection vulnerability can be exploited. Our evaluation demonstrated that the approach is effective to detect SQL injection vulnerabilities and to produce inputs that bypass application firewalls, which is a common configuration in real world.
Computing alternate multicast trees with maximum latency guarantee

The growing demand for online media content delivery and multi-player gaming is expected to increase the amount of multicast service requests in both public and private networks. Careful traffic engineering of multicast service requests is becoming increasingly essential, as establishing the lowest cost tree, e.g., shortest path tree, in the network for every individual multicast request does not always ensure a global optimization.
Detecting, categorizing and clustering entity mentions in Chinese text

The work presented in this paper is motivated by the practical need for content extraction, and the available data source and evaluation benchmark from the ACE program. The Chinese Entity Detection and Recognition (EDR) task is of particular interest to us. This task presents us several language-independent and language-dependent challenges, e.g. rising from the complication of extraction targets and the problem of word segmentation, etc. In this paper, we propose a novel solution to alleviate the problems special in the task. Mention detection takes advantages of machine learning approaches and character-based models. It manipulates different types of entities being mentioned and different constitution units (i.e. extents and heads) separately. Mentions referring to the same entity are linked together by integrating most-specific-first and closest-first rule based pairwise clustering algorithms. Types of mentions and entities are determined by head-driven classification approaches. The implemented system achieves ACE value of 66.1 when evaluated on the EDR 2005 Chinese corpus, which has been one of the top-tier results. Alternative approaches to mention detection and clustering are also discussed and analyzed.
A Clustering-Based Approach for Discovering Interesting Places in a Single Trajectory

With the development of many location sensors such as GPS technology and mobile communication devices, a lot of trajectories of users and moving objects can be obtained. These trajectories may contain many interesting individual patterns of the users and moving objects. This creates an appropriate basis for developing efficient new methods for mining moving objects. Semantic clustering of trajectories left behind moving objects is an important aspect in spatio-temporal data mining. Algorithm CB-SMoT (Clustering-Based Stops and Moves of Trajectories) is based on a traditional algorithm DBSCAN which is a classical density-based clustering approach. There is an important parameter Eps in the algorithm CB-SMoT, whose value can dramatically affect the quality of clustering. With in-depth analysis of spatial and temporal characteristics of trajectory data and some related statistical theory, the trajectory data is pre-processed. A new method of calculating the Eps value is proposed. The experiment proves that using this method to calculate the parameter values can significantly improve the quality of clustering.
ICI-Free Equalization in OFDM Systems with Blanking Preprocessing at the Receiver for Impulsive Noise Mitigation

In this letter, we consider the problem of equalizing finite-impulse response (FIR) channels in orthogonal frequency-division multiplexing (OFDM) systems, which employ at the receiver a blanking nonlinearity to mitigate impulsive noise (IN). By exploiting the frequency redundancy associated with the presence of OFDM virtual carriers, we design a frequency-domain linear FIR equalizer that compensates for the intercarrier interference (ICI) generated by nonlinear preprocessing. Monte Carlo computer simulations, carried out assuming a Middleton Class A model for the IN, allows one to assess the error probability performance of the proposed equalizer.
Modeling and control for stability and rotation velocity of a rotary inverted pendulum

The rotary inverted pendulum is a highly nonlinear, multivariable and dynamically unstable model. It is a fundamental and interesting problem in control theory. The objective of this paper is to: (i) derive the mechanical model of a rotary inverted pendulum by using Euler-Lagrange equation, (ii) design control systems for the rotary inverted pendulum with PID controller and pole placement method, and (iii) propose a simple method to accelerate the rotation speed. The effectiveness of these control techniques is shown in simulation results. An experimental prototype is also completed to test the control algorithms. With theory analysis and experiment results, it is verified that both controllers are good at maintaining the system stability while the PID method shows a better transient response. The proposed method also has a great advantage in accelerating the rotation velocity.
Optimal and Robust Transmit Designs for MISO Channel Secrecy by Semidefinite Programming

In recent years there has been growing interest in study of multi-antenna transmit designs for providing secure communication over the physical layer. This paper considers the scenario of an intended multi-input single-output channel overheard by multiple multi-antenna eavesdroppers. Specifically, we address the transmit covariance optimization for secrecy-rate maximization (SRM) of that scenario. The challenge of this problem is that it is a nonconvex optimization problem. This paper shows that the SRM problem can actually be solved in a convex and tractable fashion, by recasting the SRM problem as a semidefinite program (SDP). The SRM problem we solve is under the premise of perfect channel state information (CSI). This paper also deals with the imperfect CSI case. We consider a worst-case robust SRM formulation under spherical CSI uncertainties, and we develop an optimal solution to it, again via SDP. Moreover, our analysis reveals that transmit beamforming is generally the optimal transmit strategy for SRM of the considered scenario, for both the perfect and imperfect CSI cases. Simulation results are provided to illustrate the secrecy-rate performance gains of the proposed SDP solutions compared to some suboptimal transmit designs.
Efficient discovery of frequent unordered trees

Recently, an algorithm called Freqt was introduced which enumerates all frequent induced subtrees in an ordered data tree. We propose a new algorithm for mining unordered frequent induced subtrees. We show that the complexity of enumerating unordered trees is not higher than the complexity of enumerating ordered trees; a strategy for determining the frequency of unordered trees is introduced.
Tool Support for Collaborative Qualitative Analysis

We have previously reported on a small interview study we conducted to explore the collaborative aspects of qualitative analysis. Our findings suggest that tool support for this collaboration was limited. Our ongoing research goal is to determine how tool support can be improved. Saturate, a webbased qualitative analysis tool that is the focus of this video, represents a step in that direction.
Two-loop enhancement factor for 1/Q corrections to event shapes in deep inelastic scattering

We compute the two-loop enhancement factors for our earlier one-loop calculations of leading (1/Q) power corrections to the mean values of some event shape variables in deep inelastic lepton scattering. The enhancement is found to be equal to the universal ``Milan factor'' for those shape variables considered, provided the one-loop calculation is performed in a particular way. As a result, the phenomenology of power corrections to DIS event shapes remains largely unaffected.
Mobile apps for IBD self: management using wearable devices and sensors

Smart technology, like wearable sensors or biochips, presents a vast capacity for monitoring vital signs, assess patients' behaviour and context, and simultaneously provide feedback with a significant effect in diagnosis, treatment, and control of diseases. Many chronic disease, in particular Inflammatory Bowel Disease (IBD), patients need to monitor their behaviour and register their disease history (e.g. symptoms, medication intake), as well as collect their physiological data, in order to control the disease, find correlations between their behaviour and the disease progress and help doctors to adjust treatment and promote patients behaviour changes. We have been working in the use of m-health applications by chronic disease patients to facilitate self-management of their diseases and increase their autonomy. We are now studying the use of wearable devices and biochips to automatically collect patients' data and empower them in managing their own health conditions.
A refinement model with information granulation focused on difficult to distinguish cases

The paper proposes a different approach to data modeling. Analogous to the rejection method, where the misclassifications are removed and manually evaluated, we focus here on difficult to distinguish cases for binary classification. Such cases are further explored and information granulation is conducted based on the idea to stretch out the interesting intervals. This refinement model adopts the concept from database theory where the nxn relations are resolved through additional internal attribute connections. We introduce an integration of target functions for such cases. The achieved experimental results from a test dataset are described and future work for user assistance in knowledge modeling is presented.
Slotted Rectangular Microstrip Antenna for Dual Band Operation

ABSTRACT This paper covers two aspects of Microstrip antenna designs. The first is the analysis and design of single element rectangular Microstrip antenna which operates at the centre frequency of 2.4 GHz. The second aspect is the design of dual band rectangular microstrip antenna which operates at 2.4 GHz and 3.6 GHz used in 802.11b/g/n and 802.11y WLAN channels respectively. Both antennas are modeled, designed and simulated. Here for the rectangular patch, Inset Line Feed technique is used to design both the antennas. First, the design parameters for single element of rectangular patch antenna is calculated from the transmission line model equation and then the antenna design is extended to operate in dual band using the slots at radiating edges and near the inset feed line and obtained good radiation characteristics. The simulation process has been carried out in Advanced Design System(ADS) with the specifications ɛ r =4.6 and h=1.6mm and f r =2.4GHz.
A Dynamic Reseeding DRBG Based on SRAM PUFs

Deterministic random bit generator (DRBG) is an important building block for many cyber security systems. The secrecy of the seeds provides the basis for the security of DRBG. Periodic or dynamic reseeding is a good way of addressing the threat of the DRBG seeds, entropy input and working state being compromised over time. In this paper, the SRAM PUFs noisy cells will be used as the entropy source of DRBG. To this end, the most conservative estimate of min-entropy are tested under different temperature and reseeding times, confirm that the output of the entropy source can meet the requirements of DRBG in dynamic reseeding process. And then, min-entropy of authentication cells are tested in blocks, and discussed how to use it as nonce. Based on the tests above, this paper proposes a DRBG system which can reseed dynamically and monitor the entropy of entropy source in get entropy module online. This DRBG is implemented and tested in FPGA. 2250 times reseeding process random tests (by NIST SP 800-22 test suit) prove that this mechanism can be used in the cryptographic system.
Multi stage attack Detection system for Network Administrators using Data Mining

In this paper, we present a method to discover, visualize, and predict behavior pattern of attackers in a network based system. We proposed a system that is able to discover temporal pattern of intrusion which reveal behaviors of attackers using alerts generated by Intrusion Detection System (IDS). We use data mining techniques to find the patterns of generated alerts by generating Association rules. Our system is able to stream real-time Snort alerts and predict intrusions based on our learned rules. Therefore, we are able to automatically discover patterns in multistage attack, visualize patterns, and predict intrusions.
Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network

Wireless networks are vulnerable to identity-based attacks, including spoofing attacks, significantly impact the performance of networks. Conventionally, ensuring the identity of the communicator and detecting an adversarial presence is performed via cryptographic authentication. Unfortunately, full-scale authentication is not always desirable as it requires key management, coupled with additional infrastructural overhead and more extensive computations. The proposed non cryptographic mechanism which are complementary to authenticate and can detect device spoofing with little or no dependency on cryptographic keys. This generalized Spoofing attack-detection model utilizes MD5 (Message Digest 5) algorithm to generate unique identifier for each wireless nodes and a physical property associated with each node, as the basis for (1) detecting spoofing attacks; (2) finding the number of attackers when multiple adversaries masquerading as a same node identity; and localizing multiple adversaries. Cluster-based mechanisms are developed to determine the number of attackers. The proposed model can be explored further to improve the accuracy of determining the number of attackers, by using Support Vector Machines (SVM).
Ultra-Concise Multi-genre Summarisation of Web2.0: towards Intelligent Content Generation

The electronic Word of Mouth has become the most powerful communication channel thanks to the wide usage of the Social Media. Our research proposes an approach towards the production of automatic ultra-concise summaries from multiple Web 2.0 sources. We exploit user-generated content from reviews and microblogs in different domains, and compile and analyse four types of ultra-concise summaries: a)positive information, b) negative information; c) both or d) objective information. The appropriateness and usefulness of our model is demonstrated by its successful results and great potential in real-life applications, thus meaning a relevant advancement of the state-of-the-art approaches.
LEARNING SONG SIMILARITY FROM RADIO STATION PLAYLISTS

This extended abstract details a submission to the 2009 Music Information Retrieval eXchange (MIREX) audio music similarity and retrieval task. We propose a method for computing song similarity by learning song transition probabilities from audio features extracted from songs played in professional radio station playlists. We train a binary classifier to distinguish between 2-song sequences that were played on the radio and sequences that were not. The certainty of the trained model when presented with a song sequence can then be interpreted as the similarity between the two songs. The model used is explained in more details in [1]. 1. LEARNING FROM RADIO STATION PLAYLISTS 1.1 Defining a playlist Our similarity model is trained on professional radio station playlists. For this experiment, we consider a playlist to be a sequence of 2 consecutive plays uninterrupted by a commercial break. Suppose a radio station plays the tracks ta, tb and tc one after the other, we will consider {ta, tb} and {tb, tc} as two 2-song sequences ∈ S. We consider the sequences {ta, tb} and {tb, ta} as two distinct sequences. The model’s output will thus be non-symmetric in regards to the order in which the songs are presented. 1.2 Playlist sources We used playlist data from two sources. First, we used data from the free Internet-streamed radio station RadioParadise 1 , who provided us with 575 days worth of data. The data consists of 195,692 plays, 6,328 unique songs and 1,972 unique artists. We also used Yes.com’s API 2 , which gives access to the play history of thousands of radio stations in the United 1 http://www.radioparadise. com 2 http://api.yes. com Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. c © 2009 International Society for Music Information Retrieval. States. We mined data for 57 days, totaling in 449 stations, 6,706,830 plays, 42,027 unique songs and 9,990 unique artists. 1.3 Putting the data together Combining all the data yielded 6,902,522 plays, with an average of 15,338 plays per station. Since our model uses audio features as input, we need the audio file for all the songs we will use. Of the 47,044 total songs played in the playlists we used, we were able to obtain the audio files for 7,127 tracks. This reduced the number of distinct usable 2-song sequences to 180,232. The sequences for which we had all the audio files were combinations from 5,562 tracks. We did not possess a set of explicit negative examples (i.e. two-song sequences that a radio station would never play). In order to train the model with examples from both the positive and negative class, we considered any song sequence that was not observed as a negative example. During training, at each new epoch, we randomly sampled a new set of negative examples matched in size to our positive example set. 2. SIMILARITY MODEL OVERVIEW
MOTIVATIONAL FACTORS, FACILITATORS, OBSTACLES AND GENDER DIFFERENCES: AN EXPLORATORY STUDY OF THAI ENTREPRENEURS

The objective of this study is to identify the motives for starting a business, the perceived facilitators, obstacles and gender differences between Thai male and female entrepreneurs. A questionnaire survey was administered to entrepreneurs in Bangkok yielding a total sample of 103 respondents. Data were analyzed using cross-tabulation analysis. The results indicated that for male entrepreneurs, the desire to own a business, opportunity to earn higher income, passion for a product, the desire to be one’s own boss, taking over family business and unemployment were cited most frequently as motivators. Among the female entrepreneurs, the top motives were the desire to own a business, unemployment, the desire to be one’s own boss, passion for a product, the opportunity to increase income, and taking over family business. For women, unemployment appeared to push them to become an entrepreneur, while for men, financial motive was a pull factor. For both men and women, the factors perceived to be facilitators were mainly related to personality traits of perseverance, honesty, diligence and sincerity. Competition, low profitability, adverse economic and weather conditions were viewed as obstacles in running their business for both male and female entrepreneurs in this study. Implications of the study and recommendations for aspiring entrepreneurs are discussed.
The future of swans

Arnold Zable: This session of the Melbourne Writers Festival is sponsored by Melbourne PEN. The empty chair on stage is for a writer who can't be here because they have been imprisoned or exiled or threatened or harassed in some way. There are about a thousand cases of persecuted writers at any one time that PEN International defends.
Selection algorithms for replicated Web servers

Replication of documents on geographically distributed servers can improve both performance and reliability of the Web service. Server selection algorithms allow Web clients to select one of the replicated servers which is "close" to them and thereby minimize the response time of the Web service. Using client proxy server traces, we compare the effectiveness of several "proximity" metrics including the number of hops between the client and server, the ping round trip time and the HTTP request latency. Based on this analysis, we design two new algorithms for selection of replicated servers and compare their performance against other existing algorithms. We show that the new server selection algorithms improve the performance of other existing algorithms on the average by 55%. In addition, the new algorithms improve the performance of the existing non-replicated Web servers on average by 69%.
Using genetic algorithms to find new color transformations for JPEG-2000 image compression

Image compression techniques play an important role in multimedia applications. The JPEG-2000 which is based on the wavelet transform is a promising image compression technique expected to replace the current discrete cosine transform based compression known as JPEG. In this paper, genetic algorithms are used to optimize the coefficients of the RGB to YCbCr color transform used in JPEG-2000 and to find alternate color transforms. Color transformation in JPEG-2000 is an early phase of the compression process intended to improve the compression performance. The matrix elements are optimized using fitness functions based on the root mean square error between the original and the reconstructed image. The resultant color transformations revealed an enhancement in the JPEG-2000 codec by.
Forecasting business failure using two‐stage ensemble of multivariate discriminant analysis and logistic regression

A major drawback associated with the use of classical statistical methods for business failure prediction on top of financial distress is their lack of high accuracy rate. This work analyses the use of the two‐stage ensemble of multivariate discriminant analysis (MDA) and logit to improve predictive performance of classical statistical methods. All possible ratios are firstly built from the quantities involved and then the three common filters, that is stepwise MDA, stepwise logit, and t‐test, are used to choose another three convenient subsets of ratios. Four principal components spaces (PCSs) are, respectively, produced on the four different feature spaces by using principal components analysis. MDA and logit are used to produce predictions on the four PCSs. After that, two levels of ensemble are implemented: one based on predictions inside each of the same type of model (i.e. MDA or logit) and another based on the former two ensembles and one best model. Each of the eight models is weighted on the base of ranking order information of its predictive accuracy in ensemble by majority voting. MDA and logit and the new challenge model of support vector machine respectively in their best standalone modes are used for comparisons. Empirical results indicate that the two‐stage ensemble of MDA and logit compares favourably with the three comparative models and all its component models.
A Real Time Computer Vision System based on the Standard Model

Motivation The standard model is a biologically motivated architecture for computer vision whose components are in close agreement with existing physiological evidence. It is ’standard’ because it attempts to consolidate all the widely accepted facts and observations in a single model. The model was compared extensively to other front-end vision algorithms [7, 1, 6, 3] and has been shown to outperform state-of-the-art computer vision systems in object recognition task.