Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural networks

A variety of artificial neural networks are reviewed, including feed‐forward networks, recurrent networks, associative memories such as the Hopfield network, and the self‐organizing map. Their architectures are described, as are methods that have been developed for training them. Particular emphasis is placed on links with statistical activities, especially regression, classification, and clustering, in contexts such as graphical models and latent structure models. In terms of the training procedures, attention is drawn to the implicit or explicit implementation of statistical methodological approaches such as likelihood and Bayesian methods. Copyright © 2009 John Wiley & Sons, Inc.
V-KTPY Prefix Hashed Trie for Indian Languages: A Case Study with Kannada Text Retrieval

— The tries used in existing retrieval engines are unicode based and are more applicable to languages like English. When same method is applied to phonetic languages like Indian and South Asian languages they aren’t that efficient in terms of compact representation. Therefore, we have developed a novel V-KTPY Prefix Hashed Trie (VKTPY PHT) based approach which is efficient in memory usage (compact representation) and a general methodology for the retrieval of text in Indian and south Asian languages. In our approach we have made use of the fact that these languages are phonetic in nature and follows Brahmi/ Devanagari based scripts, which can be represented numerically by the “Katapayaadi Sankhya Sutra” (KTPY Rule). The enhancement proposed by us to this KTPY Rule is called “Vistruta Katapayaadi Sankhya Sutra” (V-KTPY Rule). Using Our V-KTPY Rule, we have developed an efficient encryption/decryption technique and have applied it to a south Indian language - Kannada as a case study. However, our technique is general and extendable to around 120 Indian and south Asian languages which follows Brahmi/Devanagari scripts. Our proposed VKTPY PHT method first converts unicode Kannada to V-KTPY encoded Kannada and stores them as prefixes, indexed by its first character set. By this, we get a 20% improvement in memory compared to existing unicode tries with same time complexity. Our proposed VKTPY PHT is not restricted to text but can also be used in speech (spoken) enabled retrieval engines for all Indian languages. We have some preliminary results to show this capability. In short, the key benefits of our approach are memory efficiency and applicability to many Indian and south Asian languages.
Discovery of usage patterns in digital library web logs using Markov modeling

This paper proposes a family of tools based on Markov modeling to quantitatively analyze how people access the digital collections of the Bibliotheque nationale de France (BnF, the national library of France), through the web platform called Gallica. The aim is to provide the BnF with relevant information about the various usage patterns to help them to better understand their users, improve the mediation efforts and the design of the website, in order to increase the general public use of the 4M-documents collection. For that purpose, the study focuses on the access logs retrieved from the Apache HTTP servers of Gallica that are converted into sequences of actions. In order to study user navigation behaviors, we propose to model the access log data using Markov Models, whether it be Markov chains when considering sequences of actions without duration, or Markov processes when taking into account duration. Our models are either used to capture an average behavior through meaningful statistics or to cluster the data to exhibit various interpretable types of usage. The numerical results bring new insights on the way the users interact with the platform, highlighting the mean duration of some actions such as the interaction with the search engine or the consultation of documents. Even if our approach requires the use of additional information in order to properly interpret the models and the correlations that it highlights, it is able to discover all types of behaviors, including the stealthiest and the most difficult to capture in traditional surveys, giving them their fair weight in terms of audience. We also show how this approach fits into a broader work combining data mining and ethnography.
Research on Robustness of Complex Networks Based on Genetic Algorithm

Complex network is widely used in networks, such as social networking services. The robustness is always a focus problem. Based on the analysis on influence of network node malfunction to the overall connectivity, we propose a node ranking strategy to optimize the robustness of complex network. This node ranking strategy is based on the Genetic Algorithm, which is applied to an unweighted graph in this paper, where the robustness value is an optimization target. Comparing the nodes in the network graph to the gene fragments, the cross mutation is realized, which ranking the nodes in the network. The simulation experiment based on the data from routing networks and a simulation of network, show the feasibility and practicability of this algorithm.
SCATTER: Fully Automated Classification System across Multiple Databases

Data mining approaches performed recently use data coming from a single table and are not adapted to multiple tables. Moreover, computer network expansion and data sources diversity require new data mining systems handling databases heterogeneity in multi-database systems. In this paper, we propose SCATTER: a fully automated classification system from multiple heterogeneous databases. SCATTER is composed of three components. The first component uses schema matching techniques to find foreign-key links across the multi-database system. The second component tries to find the most useful links that are critical for producing accurate classes across multiple databases. The last component is a decision tree classification algorithm which exploits the useful links discovered automatically across the databases. Experiments performed on real databases were very satisfactory with an average accuracy of 86.5% and showed that SCATTER system succeeded in achieving a fully automated classification from multiple heterogeneous databases.
EOG-Based Eye Movement Classification and Application on HCI Baseball Game

Electrooculography (EOG) is considered as the most stable physiological signal in the development of human–computer interface (HCI) for detecting eye-movement variations. EOG signal classification has gained more traction in recent years to overcome physical inconvenience in paralyzed patients. In this paper, a robust classification technique, such as eight directional movements is investigated by introducing a concept of buffer along with a variation of the slope to avoid misclassification effects in EOG signals. Blinking detection becomes complicated when the magnitude of the signals are considered. Hence, a correction technique is introduced to avoid misclassification for oblique eye movements. Meanwhile, a case study has been considered to apply these correction techniques to HCI baseball game to learn eye-movements.
SDTE: A Secure Blockchain-Based Data Trading Ecosystem

Data, a key asset in our data-driven economy, has fueled the emergence of a new data trading industry. However, there are a number of limitations in conventional data trading platforms due to the existence of dishonest buyer/data broker. To mitigate these limitations, we posit the importance of a data processing-as-a-service model, which complements the conventional data hosting/exchange-as-a-service model. Specifically, in this paper, we introduce a secure data trading ecosystem and present a new blockchain-based data trading ecosystem (hereafter referred to as SDTE). In the ecosystem, both data broker and buyer are not able to obtain access to the seller’s raw data, as they are only getting access to the analysis findings that they require. In other words, we reduce the challenge of securing the dataset to the challenge to secure the data processing. We also build a security model to analyze the data trading market and describe a new set of trading protocols for the entire data trading market. To demonstrate utility, we implement our proposed secure data trading platform (SDTP) on Ethereum & Intel’s Software Guard Extensions (SGX) and perform an in-depth analysis.
Modelling of Hand Gestures and Motions

Measuring the radar response has proven to be a valid input for neural networks used for gesture and motion recognition. Previous work have been based on measuring the radar response of hand gestures, but it has been shown that models based on the analytic expressions for basic objects are useful when simulating larger motions, such as pedestrians. This work explores if a hand model made of basic objects can reach the same results as actual hands. To do this, this basic objects model is implemented in two different solvers to compare the difference in value and run-time between analytic expressions and Finite Element Method solutions. The Finite Element solution is shown to change value based on the simulated gesture, but the analytic model is proving to be far faster. In the end, the latter is not capable of reproducing the response of all motions which have been measured. Implementing object interaction into the analytic model should give this model the details required to simulate even the smaller gestures. Therefore, this model is still work in progress.
On reduced Beltrami equations and linear families of quasiregular mappings

This paper studies linear classes of planar quasiregular mappings. We give a positive answer to a conjecture of K. Astala, T. Iwaniec, and G. Martin (2009) on reduced Beltrami equations. Moreover, we use it to prove a Wronsky-type theorem for general linear Beltrami systems. This is a key to show that the associated Beltrami equation of a linear quasiregular family is unique.
RGVis: Region Growing Based Visualization Techniques for Volume Visualization

Author(s): Huang, Runzhen; Ma, Kwan-Liu | Abstract: Interactive data visualization is inherently an iterative trial-and-error process searching for an ideal set of parameters for classifying and rendering features of interest in the data. This paper presents 3-d region growing based techniques that can assist the users to locate and define features of interest in volume data more quickly and more accurately. One technique employs partial region growing to generate a 2-d transfer function that effectively reveals the full features of interest. The other technique uses the result of full region growing to systematically construct a boundary surface for the extracted features. The resulting polygonal representation of the boundary surface can facilitate comparison, measurement, and simulation. A visual assessment method is suggested by using the extracted volume and surface information. These techniques either shorten or completely eliminate the typical trial-and-error step in the process of interactive data exploration.
Introducing 10-nm FinFET technology in Microwind

This paper describes the implementation of a high performance FinFET-based 10-nm CMOS Technology in the educational tool Microwind. New concepts related to the design of FinFET and design for manufacturing are also described. The performances of a ring oscillator layout and a 6-transistor RAM memory layout are also analyzed.
Chinese spam filtering based on Stacked Denoising Autoencoders

Aimed at the problem that the traditional feature selection method extracts the feature items and the filtering accuracy is degraded in the Chinese spam filtering process, this paper proposes a Chinese spam filtering method based on Stacked Denoising Autoencoder (SDA). Firstly, use the Continuous Bag-of-Words (CBOW) model training the Word2vec tool set for the processed corpus to transform word segments into vectors; the inputs are the word vectors; then apply the Stacked Denoising Autoencoder (SDA) to effectively extract the features text in unsupervised learning. Finally, the improved softmax classifier is used for regression classification. The test was carried out on the TREC06C dataset, the experimental results show that compared with Bayesian model, KNN classification algorithm and traditional Stacked Denoising Autoencoder, the accuracy, precision, recall, and f1 score of the method reached 93.5%, 94.8%, 92% and 93.2%, and had better dichotomous effect and robustness in the application.
Term evaluation metrics in imbalanced text categorization

Abstract This paper proposes four novel term evaluation metrics to represent documents in the text categorization where class distribution is imbalanced. These metrics are achieved from the revision of the four common term evaluation metrics: chi-square, information gain, odds ratio, and relevance frequency. While the common metrics require a balanced class distribution, our proposed metrics evaluate the document terms under an imbalanced distribution. They calculate the degree of relatedness of terms with respect to minor and major classes by considering their imbalanced distribution. Using these metrics in the document representation makes a better distinction between the documents of the minor and major classes and improves the performance of machine learning algorithms. The proposed metrics are assessed over three popular benchmarks (two subsets of Reuters-21578 and WebKB) by using four classification algorithms: support vector machines, naive Bayes, decision trees, and centroid-based classifiers. Our empirical results indicate that the proposed metrics outperform the common metrics in the imbalanced text categorization.
Cultural Dimensions and Modern Russian Business

Human behavior and its result in the business sphere are based on values and norms, which, in turn, have their roots in the national culture. One of the most influential concepts of comprehending national cultures and assessing their impact on business sphere was developed by worldwide famous Dutch scholar G. Hofstede during 1960-1980-ies. The main advantage of the Hofstede’s concept is that he suggested measurable constructs that describe mental programs and thus enables to compare different countries. Therefore, the purpose of this paper is to introduce the findings of a recent measurement of the Russian culture conducted at the end of year 2006 and analyze expression of the cultural dimensions in modern business practices. This article is mainly intended for Western investors, managers and scholars who participate in work in or with present day Russia. It will also be of interest to students studying comparative management.
Big Data in Homeland Security

Big Data predicts outcomes and has great impacts on knowledge discovery and value creation. It can be used to fight against terrorism and enhance Homeland Security by providing accurate predictions and patterns of terrorist activities. This paper introduces some methods and technologies used in Homeland Security. They are: biometrics, radio frequency identification (RFID), data mining, cloud computing, and Big Data. Big Data applications in Homeland Security are presented. These applications include Big Data in general security and public safety, crime prediction and aviation security, disasters, and cybersecurity and cyber defense. Big Data challenges in security are also discussed.
Connecting Source Code Changes with Reasons

Understanding the reasons behind software changes is a challenging task, as explanations are not always apparent or accessible. In addition, when third party consumers of software try to understand a change, it becomes even more difficult since they are not closely working with the code. To address these challenges, we propose a technique for explicitly connecting code changes with their reasons, referred to as Flexible Artifact Change and Traceability Support (FACTS). FACTS presents a holistic view of changes by (1) generating traceability links for code changes at different levels of abstraction and (2) tracing code changes to heterogeneously represented reasons. Our user experiment indicates that FACTS is useful in understanding code changes. Keywords-software evolution tools; software traceability; software maintenance.
Review of the quality of service scheduling mechanisms in cloud

Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.
Research on Academic Information System Unnes Using Technology Acceptance Model (TAM)

Online assessment has been carried out research on academic information system Unnes using Technology Acceptance Model (TAM) whose goal is to achieve accountability in academic application by taking the Graduate Program Unnes sampling. The method used to carry out this research through interviews/in dept interviews, documentation and questionnaires. Analyses were performed using analysis of Structural Equation Modeling (SEM) based covariance AMOS (Analysis of Moment Structures). The results obtained overall, Knowledge of Search Domain has no effect on Perceived Ease of Use. However, CMIN / DF qualiﬁed, because it has a value of≤2.
Planning scope in spoken and written sentence production

This thesis investigates two questions about the cognitive mechanisms underlying the advance preparation of sentences. First, how much planning does the language system require to begin outputting a sentence and second, how is this scope determined. Previous research has concluded that advance planning embraces less than the sentence, is determined by either content or structure of some minimal linguistic unit, and is subject to variation (V. S. Ferreira & Slevc, 2007). Unlike previous research, the presented hypotheses were evaluated in both speech and writing. This eliminates explanations in terms of mechanisms that are modality specific, and therefore not fundamental to the language production system (see Alario, Costa, Ferreira, & Pickering, 2006). In two series of three experiments I elicited short sentences in speech and writing (keyboard typing). Under controlled conditions I manipulated (a) structural and lexical properties of elicited sentences (first series, Chapter 2) and (b) conceptual properties of the sentence's message (second series, Chapter 3). Hypotheses were evaluated by measurement of the time required to initiate output of the target sentence and of eye movements to referents of this sentence (arrays of simple line drawings) shown on the computer screen. These suggested two main conclusions: (1) Consistent with some previous research advance planning scopes over coordinated noun phrases (A and the B) while lexical content requires planning for the first noun but not beyond (Chapter 2), demonstrating for the first time that this effect replicates in writing. (2) Whether or not noun phrases are preplanned beyond the first noun is determined at a conceptual level, and not at a syntactic level (Chapter 3). These findings are in line with current models of language production (Bock & Ferreira, 2014; Konopka & Brown-Schmidt, 2014) and constitute a first step towards confirming the modality independence of these models.
Automata-based Model Counting String Constraint Solver for Vulnerability Analysis

Author(s): AYDIN, Abdulbaki | Advisor(s): Bultan, Tevfik | Abstract: Most common vulnerabilities in modern software applications are due to errors in string manipulation code. String constraint solvers are essential components of program analysis techniques for detecting and repairing vulnerabilities that are due to string manipulation errors. In this dissertation, we present an automata-based string constraint solver for vulnerability analysis of string manipulating programs.Given a string constraint, we generate an automaton that accepts all solutions that satisfy the constraint. Our string constraint solver can also map linear arithmetic constraints to automata in order to handle constraints on string lengths. By integrating our string constraint solver to a symbolic execution tool, we can check for string manipulation errors in programs. Recently, quantitative and probabilistic program analyses techniques have been proposed which require counting the number of solutions to string constraints. We extend our string constraint solver with model counting capability based on the observation that, using an automata-based constraint representation, model counting reduces to path counting, which can be solved precisely. Our approach is parameterized in the sense that, we do notassume a finite domain size during automata construction, resulting in a potentially infinite set of solutions, and our model counting approach works for arbitrarily large bounds.We have implemented our approach in a tool called ABC (Automata-Based model Counter) using a constraint language that is compatible with the SMTLIB language specification used by satifiabilty-modula-theories solvers. This SMTLIB interface facilitates integration of our constraint solver with existing symbolic execution tools. We demonstrate the effectiveness of ABC on a large set of string constraints extracted from real-world web applications.We also present automata-based testing techniques for string manipulating programs. A vulnerability signature is a characterization of all user inputs that can be used to exploit a vulnerability. Automata-based static string analysis techniques allow automated computation of vulnerability signatures represented as automata. Given a vulnerability signature represented as an automaton, we present algorithms for test case generation based on state, transition, and path coverage. These automaticallygenerated test cases can be used to test applications that are not analyzable statically, and to discover attack strings that demonstrate how the vulnerabilities can be exploited. We experimentally comparedifferent coverage criteria and demonstrate the effectiveness of our test generation approach.