Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate search in misuse detection-based intrusion detection by using the q-gram distance

Intrusion detection systems have in the last years become a commonly used and important component in the perimeter security for many organizations. Such systems often use a misuse detection-based model for detection of known attack patterns from a finite signature database. By applying fault-tolerant approximate search to the IDS, unknown attacks may also be detected if they are similar enough to known attacks. This is especially efficient if the unknown attack is based on existing, known, attack. However, as the amount of attacks increases, so does the time needed to search through the signature database. Fault-tolerant search also adds complexity to the search and may therefore increase the time needed to search the signature database even further. Approximate search often uses a distance measure in its pattern matching for finding the distance between the input data and the signatures in the database. If the distance is below a pre-defined threshold, the input data and the signature is considered a match. Previous research have used the edit distance or the constrained edit distance as a distance measure for approximate search. However, both of these have a high time complexity. In this thesis we look at a fast alternative for measuring the distance in approximate search for misuse detection-based intrusion detection, called the q-gram distance. The q-gram distance is known to estimate the edit distance well for some applications. We look at how the q-gram distance can be implemented in intrusion detection as a distance measure in approximate search. We suggest a two-stage approximate search method in which the q-gram distance can be applied to approximate search in intrusion detection, and compare its data reduction, performance and accuracy against the ordinary edit distance and the constrained edit distance using SNORT rules as test data. Experimental results indicate that the q-gram distance can be used as an alternative to the constrained edit distance, and by far outperform both the ordinary edit distance and the constrained edit distance in terms of performance for intrusion detection.
Person-Oriented Approaches to Profiling Learners in Technology-Rich Learning Environments for Ecological Learner Modeling

Technology-rich learning environments (TREs) provide opportunities for learners to engage in complex interactions involving a multitude of cognitive, metacognitive, and affective states. Understanding learners’ distinct learning progressions in TREs demand inquiry approaches that employ well-conceived theoretical accounts of these multiple facets. The present study investigated learners’ interactions with BioWorld, a TRE developed to guide students’ clinical reasoning through diagnoses of simulated patients. We applied person-oriented analytic methods to multimodal data including verbal protocols, questionnaires, and computer logs from 78 task solutions. Latent class analysis, clustering methods, and latent profile analysis followed by logistic regression analyses revealed that students’ clinical diagnosis ability was positively correlated with advanced self-regulated learning behaviors, high confidence and cognitive strategy use, critical attention to experts’ feedback, and their positive emotional responses to feedback. The study results have the potential to contribute to a theory-guided approach to designing TREs with a data-driven assessment of multidimensional growth. Building on the study results, we introduce and discuss an ecological learner model for assessing multidimensional learner traits which can be used to design a TRE for adaptive scaffolding.
VSM-based Text Clustering Algorithm

Text clustering, one of the most important research braches of clustering, is the application of clustering algorithm in text processing.This paper discusses different Vector Space Model(VSM)-based clustering algorithms and presents an improved text clustering algorithm——Level-Panel(LP) algorithm.In addition, according to the effects of clustering for the corpus, it presents optimizations of clustering algorithm, including dimension determining, feature selection, etc.It is proved that LP algorithm can effectively reduce the time spending in clustering process.It is high in practicability and flexibility.
Approaches for Privacy Preserving Data Mining by Various Associations Rule Hiding Algorithms - A Survey

Yesteryears, data mining has emerged as a very popular tool for extracting hidden knowledge from collection of huge amount of data. Major challenges of data mining are to find the hidden knowledge in the data while the sensitive information is not revealed. Many Industry ,Defence ,Public Sector and Organisation facing risk or having security issue while sharing their data so it is very crucial concern how to protect their sensitive information due to legal and customer concern. Many strategies have been proposed to hide the information containing sensitive data. Privacy preserving data mining is an answer to such problems. Association rule hiding is one of the PPDM techniques to protect the sensitive association rule . In this paper, all the approaches for privacy preserving data mining have been compared theoretically and points out their pros and cons.
El crecimiento económico en América Latina después de una década de reformas estructurales

“? Mejor o peor que antes? Esta es la pregunta que ronda los medios politicos y publicos de la region despues de una decada de reformas, cuyos efectos no han colmado las expectativas que generaron. Este estudio encuentra que, sin las reformas estructurales de la ultima decada, el ingreso per- capita en America Latina seria 12% inferior, y el potencial de crecimiento del PIB hacia el futuro seria del1 ,9% anual en promedio en los paises de la region. Sin las reformas, la productividad conjunta del trabajo y el capital habria continuado cayendo como venia ocurriendo desde los setentas, y las tasas de inversion se habrian estancado en niveles inferiores en promedio al 17% del PIB. Desde este punto de vista, la situacion es mejor que antes. Pero las reformas adelantadas hasta ahora son insuficientes para regresar a las tasas de crecimiento del orden del 5% que fueron usuales en el pasado, y menos aun para lograr los niveles superiores al 7% comunes en Asia del Este. Con las politicas actuales, las economias de la region pueden aspirar a crecer en promedio a un ritmo de solo 3,8% anual. Desde este otro punto de vista, la situacion actual es peor. Estas conclusiones provienen de un analisis sistematico de los efectos de las reformas estructurales adoptadas en 19 paises de la region entre 1985 y 1995. Las deficiencias de educacion son el principal obstaculo para lograr tasas mayores de crecimiento, especialmente en los paises mas pobres. La educacion acelera el crecimiento a traves de su mayor contribucion directa a la acumulacion de capital humano e indirecta a traves de su impacto favorable sobre la productividad. ”
Development and Analysis of Stochastic Boundary Coverage Strategies for Multi-Robot Systems

Robotic technology is advancing to the point where it will soon be feasible to deploy massive populations, or swarms, of low-cost autonomous robots to collectively perform tasks over large domains and time scales. Many of these tasks will require the robots to allocate themselves around the boundaries of regions or features of interest and achieve target objectives that derive from their resulting spatial configurations, such as forming a connected communication network or acquiring sensor data around the entire boundary. We refer to this spatial allocation problem as boundary coverage. Possible swarm tasks that will involve boundary coverage include cooperative load manipulation for applications in construction, manufacturing, and disaster response. In this work, I address the challenges of controlling a swarm of resource-constrained robots to achieve boundary coverage, which I refer to as the problem of stochastic boundary coverage. I first examined an instance of this behavior in the biological phenomenon of group food retrieval by desert ants, and developed a hybrid dynamical system model of this process from experimental data. Subsequently, with the aid of collaborators, I used a continuum abstraction of swarm population dynamics, adapted from a modeling framework used in chemical kinetics, to derive stochastic robot control policies that drive a swarm to target steady-state allocations around multiple boundaries in a way that is robust to environmental variations. Next, I determined the statistical properties of the random graph that is formed by a group of robots, each with the same capabilities, that have attached to a boundary at random
Interpreting Our Heritage

Thank you for reading interpreting our heritage. Maybe you have knowledge that, people have search hundreds times for their chosen novels like this interpreting our heritage, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some harmful bugs inside their laptop. interpreting our heritage is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the interpreting our heritage is universally compatible with any devices to read.
Outburst Topic Detection for Web Forums

Web forum has become an important resource on the Web due to its rich information contributed by millions of Internet users every day.Consequently,the outburst topic detection becomes a fundamental task in Search Engine and Web Mining systems.Most existing topic detection and tracking(TDT) methods deal with the news stories,which are proved not suitable for extracting topics in casual,oral and informal languageon the noisy Web formus.This paper presents a noise-filtered model to extract the outburst topics from web forums using terms and participations of users.The proposed model employs not only content similarity,but also user participation information.Experiments on ShuiMu community demonstrate the efficiency of the proposed model: not only extracting the outburst topics which are better organized for search and visualization but also discovering communities corresponding to these topics.
Recommender systems using cluster-indexing collaborative filtering and social data analytics

As a result of the extensive variety of products available in e-commerce settings during the last decade, recommender systems have been highlighted as a means of mitigating the problem of information overload. Collaborative filtering (CF) is the most widely used algorithm to build such systems, and improving the predictive accuracy of CF-based recommender systems has been a major research challenge. This research aims to improve the prediction accuracy of CF by incorporating social network analysis (SNA) and clustering techniques. Our proposed model identifies the most influential people in an online social network by SNA and then conducts clustering analysis using these people as initial centroids (cluster centres). Finally, the model makes recommendations using cluster-indexing CF based on the clustering outcomes. In this step, our model adjusts the effect of neighbours in the same cluster as the target user to improve prediction accuracy by reflecting hidden information about his or her social community. The experimental results indicate that the proposed model outperforms other comparison models, including conventional CF, with statistical significance.
Face pose estimation using PCA

Face recognition has been one among the foremost attention-grabbing and vital analysis fields within the past 20 years. The explanations come back from the necessity of automatic recognitions and police work systems, the interest in human sensory system on face recognition, and also the style of human-computer interface, etc. These researches involve information and researchers from disciplines like neurobiology, psychology, laptop vision, pattern recognition, image process, and machine learning, etc. Different techniques like principal component analysis (PCA), 2PCA, kernel principal component analysis (KPCA) are used for recognition. Distinction factors like illumination, pose, and expression are degrading the accuracy of recognition. In this paper we have taken 11 poses of a given subject before recognition and performed pose estimation for identifying correct pose of image, hence increasing the accuracy of recognition of image.
A Method of Sketch-Based Human Body Modeling

This paper presents a method of sketch-based human body modeling, which allows users to rapidly construct human model by sketching human skeleton and contours separately. The are two main features of our method: firstly, a multi-layered cross-section based human template is used to construct the result model, and a distance-based descriptor is computed in the model retrieval process to find the template most consistent with user's sketch; secondly, a parameter-driven method of 3D human body modeling is used, it matches the sketch contour features and template contour features to compute 3D deformation parameters, by which the result model is constructed. The experiment result proves that the proposed method can rapidly construct 3D human body model which consistent with user's intent.
Evaluation of Topic Models as a Preprocessing Engine for the Knowledge Discovery in Twitter Datasets

In the World Wide Web, users are an important information source for companies or institutions. People use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments of products, politics, society, or even private situations. In 2014, the Twitter users worldwide submitted 582 million messages (tweets) per day. To process the mass of Web 2.0’s data (e
Application of Image Stitching in the Scene Investigation of Traffic Accident

Image stitching technology is to stitch some picture into a whole seamless view with no loss of resolution. In order to meet the random objective conditions and the characteristics of high real-time demand in traffic accident scene image stitching, an improved Harris corner algorithm has been proposed in this paper. The algorithm reduces the impact from parameter setting itself on extracting corners. And can complete image registration accurately, by using a smooth transition of fusion algorithm, the algorithm achieve a seamless image.
Domain Adaptation Method for LHMM-based English Part-of-Speech Tagger

A large number of current language processing systems use a part-of-speech tagger for preprocessing. Most language processing systems required a tagger with the highest possible accuracy. Specially, the use of domain-specific advantages has become a hot issue in machine translation community to improve the translation quality. This paper addresses a method for customizing an HMM or LHMM based English tagger from general domain to specific domain. The proposed method is to semi-automatically customize the output and transition probabilities of HMM or LHMM using domain-specific raw corpus. Through the experiments customizing to Patent domain, our LHMM tagger adapted by the proposed method shows the word tagging accuracy of 98.87% and the sentence tagging accuracy of 78.5%. Also, compared with the general tagger, our tagger improved the word tagging accuracy of 2.24% (ERR: 66.4%) and the sentence tagging accuracy of 41.0% (ERR: 65.6%).
Non-Iterative Wide-Modulation-Index Switching-Angle Calculation Techniques for 15-Level Binary Cascaded H-Bridge Multilevel Inverter

Cascaded H-bridge multilevel inverter (CHBMI) is able to generate a staircase AC output voltage with low switching losses. The switching angles applied to the CHBMI have to be calculated and arranged properly in order to minimize the total harmonic distortion (THD) of the output voltage waveform. In this paper, two non-iterative switching-angle calculation techniques applied for a 15-level binary asymmetric CHBMI are proposed. Both techniques employ a geometric approach to estimate the switching angles, and therefore, the generated equations can be solved directly without iterations, which are usually time-consuming and challenging to be implemented in real-time. Apart from this, both techniques are also able to calculate the switching angles for a wide range of modulation index. The proposed calculation techniques have been validated via MATLAB simulation and experiment. Full Text: PDF DOI: http://dx.doi. org/10.11591/ijpeds.v8. i1.pp93-99
How to design good learning agents in organization

This paper categorizes four types of multiagent learning in terms of both goals and evaluations in agents, and investigates the characteristics of each categorization to find an effective type for designing learning agents. Since the characteristics in this categorization are affected by the learning mechanisms of agents, the characteristics are investigated by referring to organizational learning in organization and management science as one of methods. Through intensive simulations on a complex domain problem, the following implication has been revealed: agents that pursue their own goals and are evaluated according to their total results show high performance in comparison with other types of agents when all learning mechanisms in the organizational learning are employed.
Sociobiological Perspectives On Human Development

sociobiological perspectives on human development is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the sociobiological perspectives on human development is universally compatible with any devices to read.
Bounding Resource Contention Interference in the Next-Generation Microprocessor (NGMP)

The Space industry, as several other real-time industries , is assessing the use of multicore processors as their main computing platform. While multicore processors bring the potential of integrating several software (mixed-criticality) functions , their use also brings some challenges. In particular, tasks running in multicores may experience high contention delays when accessing multicores' shared resources. This makes that the load that a task puts on shared resources impacts the Execution Time Bounds (ETBs) derived for other corunning tasks. In this paper we focus on the Cobham Gaisler NGMP – acknowledged as one of the multicore processors currently assessed by the European Space Agency for its future missions – for which we propose a measurement-based approach to bound contention interference. Given a task τ , instead of providing ETBs for the highest contention that any set of corunners can generate – already shown to be potentially high – our approach provides bounds that factor in the number of requests contenders generate regardless of how they align with τ 's requests. This provides a good balance between ETBs accuracy and independence from the corunners, since our approach only requires controlling the number of requests each task makes to the shared resources.
Learning by doing: A baseline approach to the clustering of web peoplesearch results

This paper has two readings. The first one consists in the description of the technical approach followed in the development of a system participating at the 2009 Web People Search Clustering Task [2]. The second reading is about an educational experiment by which a group of 12 students following a course on Intelligent Systems (in the last year of their MSc degree in Telecommunication Engineering) have fully developed such a system. Technically, the system can be defined as a baseline for future research, with basic and easy to implement approaches for every system module.
Temporal perspectives on Still Bay point production at Sibudu Cave, KwaZulu-Natal, in the context of southern Africa

ABSTRACT Based on optically stimulated luminescence age estimates it has been argued that the Still Bay represents a sudden, short-lived technological innovation dating to about 72–71 kya. Yet, few sites have the stratigraphic integrity and Still Bay point assemblage size to test this assumption. The Wadley deep sounding of Sibudu Cave provides such an opportunity. Here we use fine-grained analyses consisting of technological attributes and morphometric data to explore the retouched point assemblages of Sibudu over a period of more than ten thousand years spanning the Still Bay. Although we found subtle changes through time, we found no evidence of a technological break in retouched point-production strategies since the Wadley early pre-Still Bay at more than 77 kya through to the final Still Bay/early Howiesons Poort dating to 64.7 ± 2.3 kya. We did, however, uncover a potential point-production hiatus at the site and we present testable hypotheses for this phenomenon. We further contextualise the Sibudu assemblages within southern Africa by directly comparing them with those of Hollow Rock Shelter, Umhlatuzana and Apollo 11. Although our results demonstrate both variation and similarities between the different Still Bay assemblages, we could not replicate a previous suggestion regarding technical disconnection between a north-east/south-west axis on the greater landscape.
Urbanisation and dispossession

It was a grey morning, but the mood in the crowd was buoyant. Perhaps it was the numbers - thousands of people (maybe a record? ) assembled in front of Parliament House in Melbourne to mark the date: 26 January 2017. Invasion Day. Survival Day. Day of Mourning. For the thousands of us assembled, there was a collective refusal to call it Australia Day. High above all the banners and signs, something caught my eye - Australian flags draped over the railings around the rooftop beer garden of the pub on the corner. Onlookers leaned casually on the railing, peering over at us. The Imperial Hotel never seemed so aptly named.
Using Multi-Arm Bandits to Optimize Game Play Metrics and Effective Game Design

 Abstract — Game designers have the challenging task of building games that engage players to spend their time and money on the game. There are an infinite number of game variations and design choices, and it is hard to systematically determine game design choices that will have positive experiences for players. In this work, we demonstrate how multi-arm bandits can be used to automatically explore game design variations to achieve improved player metrics. The advantage of multi-arm bandits is that they allow for continuous experimentation and variation, intrinsically converge to the best solution, and require no special infrastructure to use beyond allowing minor game variations to be deployed to users for evaluation. A user study confirms that applying multi-arm bandits was successful in determining the preferred game variation with highest play time metrics and can be a useful technique in a game designer's toolkit.
Adapting Cracow’s Stare Debniki Area to the Mobility Needs of Sight-Impaired People

Abstract An ageing society and the predicted increase in the visual impairment of older people requires attention to be paid to the accessibility and social inclusivity of urban spaces. This paper reviews spatial barriers with particular emphasis on the limited mobility of people with visual dysfunction. Furthermore, by the example of the Stare Dębniki area in Cracow, the paper includes improvement opportunities related to the accessibility of urban spaces. The detailed let’s make noise project was developed at the ‘Przepis na miasto’ workshop.