Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards freedom of speech on the internet: censorship-resistant communication and storage

This work explores the problem space of censorship resistance with the explicit goal of protecting a censorship-resistant system and its users from powerful adversaries who control the network gateways. The result of this work is a document storage system which is highly available and robust to targeted censorship. It is designed to resist attacks from very powerful adversaries, who are willing to shut down large sections of the Internet in order to accomplish their censorship goals. Our design aims to be as easy to use, but far more robust than, some current centralized systems, so we use a completely distributed peer-to-peer infrastructure but still support human-readable keyword search. Network participants who contribute storage enjoy plausible deniability, in that they have no easy way to determine what content they are storing locally. We also explicitly support edited content, such that any information can be published, but only popular or editor-approved information will be kept. A major building block of our system is membership concealment — the idea of a network that hides the real-world identities of participants. We formalize the concept of membership concealment, show that it is required for censorship resistance, discuss a number of attacks against existing systems, and present real-world attack results. Since membership concealment requires resisting hypothesis testing and brute-force scanning, we ensure that network members are not identifiable as such by unauthorized parties. To that end, we construct an authenticated transmission control protocol, adding steganographic authentication to TCP in a provably undetectable manner. Finally, we show through theoretical analysis and simulation that the complete system, while imposing a factor of 10 storage overhead, can tolerate node failure rates up to 70% while retaining the ability to route messages and retrieve every stored file with probability 99.99998666%, even when the volume of stored content is on the order of hundreds of exabytes.
A Decision-Making Model for Safe and Effective Use of Functional Analysis Procedures

While functional analysis provides the standard methodology for the assessment of problem behavior, procedures still pose potential risk when assessing severe problem behavior. Previous studies have examined several strategies to improve the efficiency of and reduce risks related to the functional analysis process. The purpose of the present series of studies was to replicate and extend previous research on the assessment and treatment of severe problem behavior for one participant diagnosed with an intellectual disability. By incorporating strategies within a systematic decision-making framework, functional analysis procedures were implemented to conduct the assessment of precursors, determine maintaining contingencies for severe problem behavior, and evaluate the effects of a treatment based on the results of a functional analysis.
Image processing methods to segment speech spectrograms for word level recognition

The ultimate goal of automatic speech recognition (ASR) research is to allow a computer to recognize speech in real-time, with full accuracy, independent of vocabulary size, noise, speaker characteristics or accent. Today, systems are trained to learn an individual speaker's voice and larger vocabularies statistically, but accuracy is not ideal. A small gap between actual speech and acoustic speech representation in the statistical mapping causes a failure to produce a match of the acoustic speech signals by Hidden Markov Model (HMM) methods and consequently leads to classification errors. Certainly, these errors in the low level recognition stage of ASR produce unavoidable errors at the higher levels. Therefore, it seems that ASR additional research ideas to be incorporated within current speech recognition systems. This study seeks new perspective on speech recognition. It incorporates a new approach for speech recognition, supporting it with wider previous research, validating it with a lexicon of 533 words and integrating it with a current speech recognition method to overcome the existing limitations. The study focusses on applying image processing to speech spectrogram images (SSI). We, thus develop a new writing system, which we call the Speech-Image Recogniser Code (SIR-CODE). The SIR-CODE refers to the transposition of the speech signal to an artificial domain (the SSI) that allows the classification of the speech signal into segments. The SIR-CODE allows the matching of all speech features (formants, power spectrum, duration, cues of articulation places, etc.) in one process. This was made possible by adding a Realization Layer (RL) on top of the traditional speech recognition layer (based on HMM) to check all sequential phones of a word in single step matching process. The study shows that the method gives better recognition results than HMMs alone, leading to accurate and reliable ASR in noisy environments. Therefore, the addition of the RL for SSI matching is a highly promising solution to compensate for the failure of HMMs in low level recognition. In addition, the same concept of employing SSIs can be used for whole sentences to reduce classification errors in HMM based high level recognition. The SIR-CODE bridges the gap between theory and practice of phoneme recognition by matching the SSI patterns at the word level. Thus, it can be adapted for dynamic time warping on the SIR-CODE segments, which can help to achieve ASR, based on SSI matching alone.
On the Design of Real-Time Systems on Multi- Core Platforms under Uncertainty

OF THE DISSERTATION ON THE DESIGN OF REAL-TIME SYSTEMS ON MULTI-CORE PLATFORMS UNDER UNCERTAINTY by Tianyi Wang Florida International University, 2015 Miami, Florida Professor Gang Quan, Major Professor Real-time systems are computing systems that demand the assurance of not only the logical correctness of computational results but also the timing of these results. To ensure timing constraints, traditional real-time system designs usually adopt a worst-case based deterministic approach. However, such an approach is becoming out of sync with the continuous evolution of IC technology and increased complexity of real-time applications. As IC technology continues to evolve into the deep submicron domain, process variation causes processor performance to vary from die to die, chip to chip, and even core to core. The extensive resource sharing on multicore platforms also significantly increases the uncertainty when executing real-time tasks. The traditional approach can only lead to extremely pessimistic, and thus, unpractical design of real-time systems. Our research seeks to address the uncertainty problem when designing real-time systems on multi-core platforms. We first attacked the uncertainty problem caused by process variation. We proposed a virtualization framework and developed techniques to optimize the system’s performance under process variation. We further studied the problem on peak temperature minimization for real-time applications on multi-core platforms. Three heuristics were developed to reduce the peak temperature for real-time systems. Next, we sought to address the uncertainty problem
Turbo-coded optical direct-detection CDMA system with PPM modulation

The performance of an optical code division multiple access (CDMA) system with turbo coding is analyzed and simulated. Turbo codes are parallel concatenated convolutional codes (PCCCs) in which the information bits are first encoded by a recursive systematic convolutional code, and then, after passing through an interleaver, are encoded by a second systematic convolutional encoder. Turbo coding is superimposed on an intensity-modulated optical channel with pulse-position modulation (PPM) and direct detection of the received optical signal, and the performance is evaluated in terms of an upper bound on bit error probability. From the simulation results, it is seen that turbo coding offers considerable coding gain over other methods, with reasonable encoding/decoding complexity. Also, it is demonstrated that, for a fixed code rate, the performance of the optical CDMA system can be substantially improved by increasing the interleaver length and the number of iterations. Serially concatenated convolutional codes (SCCCs) are also considered, and are shown to perform comparably to PCCC in general and better than PCCCs for the case of large signal photocounts. The results in this paper can be applied, for example, to indoor optical wireless LANs.
2003 Special Issue Neural networks in astronomy

In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno). q 2003 Published by Elsevier Science Ltd.
The storage of newly learned information in semantic memory

Three experiments (no. 1, 2 and 3) were carried out in which prime-target pairs were repeatedly presented in a lexical decision task. In all experiments word targets were preceded by a) preexperimentally related primes (SEM), or b) preexperimentally unrelated primes (EPIS), or c) neutral primes (NEU), these all being the word blank (Experiments 1 and 2) or changed primes (Experiment 3). The aim of the experiments was to find sufficient learning conditions for the forming of a semantic association between a preexperimentally unrelated prime and target. The learning conditions that were investigated were a) repetition, b) paired-associate learning and c) semantic processing with pseudowords as primes, in Experiments 1, 2 and 3 respectively. Although none of the experiments revealed evidence for semantic learning, there were nonetheless effects of episodic learning. That is, preexperimentally unrelated prime-target pairs (EPIS) were facilitated relative to neutral pairs by mere repetition and by paired-associate learning. Preexperimentally related prime-target pairs (SEM) showed episodic priming only with paired-associate learning. Episodic priming was always found relative to a NEU condition with all primes being blank.
FORMAL MODELING, MONITORING, AND CONTROL OF EMERGENCE IN DISTRIBUTED CYBER PHYSICAL SYSTEMS

Cyber-physical systems (CPS) consist of physical entities that obey dynamical laws and interact with software components. A typical CPS implementation includes a discrete controller, where software periodically samples physical state and produces actuation commands according to a real-time schedule. Such a hybrid system can be modeled formally as a hybrid automaton. However, reachability tools to verify specifications for hybrid automata do not perform well on such periodicallyscheduled models. This is due to a combination of the large number of discrete jumps and the nondeterminism of the exact controller start time. In this paper, we demonstrate this problem and propose a solution, which is a validated abstraction mechanism where every behavior of the original sampled system is contained in the behaviors of a purely continuous system with an additive nondeterministic input. Reachability tools for hybrid automata can better handle such systems. We further improve the analysis by considering local analysis domains. We automate the proposed technique in the Hyst model transformation tool, and demonstrate its effectiveness in a case study analyzing the design of a yaw-damper for a jet aircraft.
THE EFFECT OF SCAFFOLDING ON READING COMPREHENSION OF VARIOUS TEXT MODES ON IRANIAN EFL LEARNERS WITH DIFFERENT PROFICIENCY LEVELS

The sociocultural theory of L. S. Vygotsky is shaped by the notion that all human learning is driven by the social interaction. The same is true for learning reading comprehension. This quantitative study drew upon experimental design to examine the effects of scaffolding language on learning reading comprehension of various text modes on Iranian EFL learners with different levels of language proficiency. One hundred eighty (180) EFL learners were randomly selected and divided into three groups of low, mid and high proficiency through the TOEFL language proficiency test. They were taught different text types such as narrations, argumentations, descriptions and explanations. The scaffolded groups were exposed to a constructivist- interactive model of learning while the non scaffolded groups were subjected to the traditional individual reading. At the end of the treatment provision period, a post test was administered. A two way ANOVA was performed. The findings suggest a choice in favor of scaffolded narrative text types for mid-level of learners.
Discovering two almost extinct languages in Bolivia: Jorá and Guarasu

The work with almost extinct languages demands special strategies, and linguists are confronted with a number of limits in documenting and describing such languages. This paper presents two case studies of almost extinct Bolivian Tupi-Guarani languages, Jora and Guarasu. The paper focuses on accounts of ethically difficult situations and discusses how the linguists have dealt with these challenges. It then shows our linguistic analysis of very limited datasets we have gathered and how with evidence from phonetics, morphology and lexicon, we can suggest an internal classification for these Tupi-Guarani languages.
Feasibility of Using Virtual Reality in Requirements Elicitation Process

ing further, all information has been averaged between groups for all routes in Figure 14; in other words, the first bar represents an average of all routes in Group 1 along with one standard deviation error line. Lower numbers in the graph once again represent a lower number of landmarks “not found”; hence, more landmarks (requirements) found. On average across all routes, the number of turns is 7.2 turns. Group 1 on average, for all routes, was not able to find landmarks for 1.867 turns; Group 2 was not able to find landmarks for 1.035 turns; and Group 3 was not able to find landmarks for 0.709 turns. However, visually looking at the standard deviations on the graph presents a threat to the validity of the experiment, therefore, generalized statements about VR and it’s performance for picking landmarks cannot
Motor bearing damage detection using stator current monitoring

This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. Vibration monitoring of mechanical bearing frequencies is currently used to detect the presence of a fault condition. Since these mechanical vibrations are associated with variations in the physical air gap of the machine, the air gap flux density is modulated and stator currents are generated at predictable frequencies related to the electrical supply and vibrational frequencies. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. The bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. Experimental results which show the vibration and current spectra of an induction machine with different bearing faults are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault. <<ETX>>
Development of New Bioinformatic Approaches for Human Genetic Studies

Background: Intellectual disabilities (ID) are neurodevelopmental disorders with an estimated prevalence in the population of 1-3%. These disorders have a complex genetic etiology, with possible involvement of not only protein-coding genes but also non-coding elements, including long non-coding RNAs (lncRNAs). However, the role of lncRNAs in ID is still unclear although evidence supports possible associations of lncRNAs with disease causality and development. To identify candidate lncRNAs associated with ID, predictive methods need to be based on gene expression patterns. Results: We have developed an expression-based classification model using Support Vector Machines. The model was trained using a developmental brain transcriptome dataset, with known ID-causing genes as the positive instances and non-ID disease genes as the negatives. We used Random Forest-based methods for
Evaluating the power efficiency and performance of multi-core platforms using HEP workloads

As Moore's Law drives the silicon industry towards higher transistor counts, processor designs are becoming more and more complex. The area of development includes core count, execution ports, vector units, uncore architecture and finally instruction sets. This increasing complexity leads us to a place where access to the shared memory is the major limiting factor, resulting in feeding the cores with data a real challenge. On the other hand, the significant focus on power efficiency paves the way for power-aware computing and less complex architectures to data centers. In this paper we try to examine these trends and present results of our experiments with Haswell-EP processor family and highly scalable HEP workloads.
ADDIE Model Application Promoting Interactive Multimedia

This paper presents the benefits of interactive learning in a vocational high school, which is developed by Research and Developmet (R&D) method. The questionnaires, documentations, and instrument tests are used to obtain data and it is analyzed by descriptive statistic. The results show the students’ competence is generated up to 80.00 %, and the subject matter aspects of the content is up to 90.00 %. The learning outcomes average is 85. This type media fulfils the proposed objective which can enhance the learning outcome.
A Multimodal Sensor Fusion Architecture for Audio-Visual Speech Recognition

A key requirement for developing any innovative system in a computing environment is to integrate a sufficiently friendly interface with the average end user. Accurate design of such a user-centered interface, however, means more than just the ergonomics of the panels and displays. It also requires that designers precisely define what information to use and how, where, and when to use it. Recent advances in user-centered design of computing systems have suggested that multimodal integration can provide different types and levels of intelligence to the user interface. The work of this thesis aims at improving speech recognition-based interfaces by making use of the visual modality conveyed by the movements of the lips. Designing a good visual front end is a major part of this framework. For this purpose, this work derives the optical flow fields for consecutive frames of people speaking. Independent Component Analysis (ICA) is then used to derive basis flow fields. The coefficients of these basis fields comprise the visual features of interest. It is shown that using ICA on optical flow fields yields better classification results than the traditional approaches based on Principal Component Analysis (PCA). In fact, ICA can capture higher order statistics that are needed to understand the motion of the mouth. This is due to the fact that lips movement is complex in its nature, as it involves large image velocities, self occlusion (due to the appearance and disappearance of the teeth) and a lot of non-rigidity. Another issue that is of great interest to audio-visual speech recognition systems designers is the integration (fusion) of the audio and visual information into an automatic speech recognizer. For this purpose, a reliability-driven sensor fusion scheme is developed. A statistical approach is developed to account for the dynamic changes in reliability. This is done in two steps. The first step derives suitable statistical reliability measures for the individual information streams. These measures are based on the dispersion of the N-best hypotheses of the individual stream classifiers. The second step finds an optimal mapping between the reliability measures and the stream weights that maximizes the conditional likelihood. For this purpose, genetic algorithms are used. The addressed issues are challenging problems and are substantial for developing an audio-visual speech recognition framework that can maximize the information gather about the words uttered and minimize the impact of noise.
CellularAutomataLib2: improving the support for cellular automata modelling in Modelica

ABSTRACT CellularAutomataLib is a library developed by the authors to facilitate the description of Cellular Automata (CA) models in Modelica. It supports the description of 1D and 2D CA and their combination with other Modelica models. Modelling versatility and scalability are the main focus in the design. The internal behaviour of CA models is programmed in C, that is consequently hidden to the modelling tool and not considered in the causalization and manipulation of the model. A new version, named CellularAutomataLib2, is presented in this manuscript. The library has been extended to improve the simulation performance, by only evaluating active cells, and to support Lattice Gas Cellular Automata (LGCA) models. The library design and use are discussed. Two models, forest fire spread and the ARGESIM C17 ‘SIR-type Epidemic Spread’, are used to illustrate the functionality of the library. CellularAutomataLib2 is freely available at http://www.euclides. dia.uned. es (under the Modelica License 2).