Miracle-dz/UniFAR-FTUs huggingface dataset:
Multicore DSP: From Algorithms to Real-time Implementation on the TMS320C66x SoC

Written by Texas Instruments’ First DSP Educator Award winner, Naim Dahnoun, the book teaches readers how to use the development tools, take advantage of the maximum performance and functionality of this processor and have an understanding of the rich content which spans from architecture, development tools and programming models, such as OpenCL and OpenMP, to debugging tools. It also covers various multicore audio and image applications in detail. Additionally, this one-of-a-kind book is supplemented with:
MapReduce: State-of-the-Art and Research Directions

Digital data that come from different applications such as, wireless sensor, bioinformatics next generation sequencing, and high throughput instruments are growing in high rate. Dealing with demands of analysis of ever-growing data requires new techniques in software, hardware, and algorithms. MapReduce is a programming model initiated by Google’s Team for processing huge datasets in distributed systems; it helps programmers to write programs that process big data. The aim of this paper is to investigate MapReduce research trends, and current research efforts for enhancing MapReduce performance and capabilities. This Study concluded that the research directions of MapReduce concerned with either enhancing MapReduce programming model or adopting MapReduce for deploying existing algorithm to run with MapReduce programming model.
Data driven hyperparameter optimization of one-class support vector machines for anomaly detection in wireless sensor networks

One-class support vector machines (OCSVM) have been recently applied to detect anomalies in wireless sensor networks (WSNs). Typically, OCSVM is kernelized by radial bais functions (RBF, or Gausian kernel) whereas selecting Gaussian kernel hyperparameter is based upon availability of anomalies, which is rarely applicable in practice. This article investigates the application of OCSVM to detect anomalies in WSNs with data-driven hyperparameter optimization. Specifically, the information of the farthest and the nearest neighbors of each sample is used to construct the objective cost instead of labeling based metrics such as geometric mean accuracy (G-mean) or area under the receiver operating characteristic (AUROC). The efficiency of this method is illustrated over the IBRL dataset whereas the resulting estimated boundary as well as anomaly detection performance are comparable with existing methods.
Demand Side Management Based Optimal Scheduling Portfolio of a Microgrid in Linear Programming Platform

Microgrid facilitates penetration of renewable energy sources into the existing distribution systems to reduce the overall carbon footprint of the globe by reducing the dependency on the main grid. To justify the large deployment of the microgrid concept, economic operation should be guaranteed. But non-linearities present in the nature of microgrid components and network make the scheduling process complex. Again, non-linear solution strategies do not guarantee global convergence. Therefore, efficient but linear model for microgrid resource scheduling algorithms are gaining interest in present time due to its simplicity and fast computation. On the contrary, for peak demand management, flexible load scheduling is a viable and easiest option to the microgrid operators to minimize the customers' dissatisfaction. This article serves the aforementioned purposes by designing a linear model for microgrid scheduling by implementing demand side management strategies to manage flexible appliances aiming for peak demand reduction. The strategy is implemented on a practical Indian distribution system consisting of commercial and residential loads to prove its efficacy.
Structural translation with synchronous tree adjoining grammars in VERBMOBIL

The VERBMOBIL project is developing a translation system that can assist a face-to-face dialogue between two non-native english speakers. Instead of having continiously speak english, the dialogue partners have the option to switch to their respective mother tongues (currently german or japanese) in cases where they can't find the required word, phrase or sentence. In such situations, the users activate VERBMOBIL to translate their utterances into english. A very important requirement for such a system is realtime processing. Realtime processing is essentially necessary, if such a system is to be smoothly integrated into an ongoing communication. This can be achieved by the use of anytime processing, which always provides a result. The quality of the result however, depends on the computation time given to the system. Early interruptions can only produce shallow results. Aiming at such a processing mode, methods for fast but preliminary translation must be integrated into the system assisted by others that refine these results. In this case we suggest structural translation with Synchronous Tree Adjoining Grammars (S-TAGs), which can serve as a fast and shallow realisation of all steps necessary during translation, i.e. analysis, transfer and generation, in a system capable of running anytime methods. This mode is especially adequate for standardized speech acts and simple sentences. Furthermore, it provides a result for early interruptions of the translation process. By building an explicit linguistic structure, methods for refining the result can rearrange the structure in order to increase the quality of the translation given extended execution time. This paper describes the formalism of S-TAGs and the parsing algorithm implemented in VERBMOBIL. Furthermore the language covered by the german grammar is described. Finally we list examples together with the execution time required for their processing.
Validity Testing for Coverage Properties of Small Area Models for Cell Proportions

In Gilary, Maples, and Slud (2012), we compared three possible models for small proportions in small survey domains: Fay-Herriot (Fay and Herriot 1979), GLMM (Jiang and Lahiri 2006), and Beta-Binomial (Prentice 1986). The comparisons used bootstrap-based condence intervals which were justied by asymptotic theory or established only for large samples. Here we build upon that work by conducting simulations of small area data in moderate-sample settings, for two purposes: to evaluate the performance of dierent analysis methods when using each of the three simulation models; and to assess the validity of predictors and Wald-type condence interval coverage properties for each method.
Securing Logs in Operation-based Collaborative Editing

In recent years collaborative editing systems such as wikis, GoogleDocs and version control systems became very popular. In order to improve reliability, fault-tolerance and availability shared data is replicated in these systems. User misbehaviors can make the system inconsistent or bring corrupted updates to replicated data. Solutions to secure data history of state-based replication exist, however they are hardly applied to operation-based replication. In this paper we propose an approach to secure log in operation-based optimistic replication system. authenticators based on hash values and digital signatures are generated each time a site shares or receives new updates on replicas. authenticators secure logs with security properties of integrity and authenticity. We present in detail algorithms to construct and verify authenticators and we analyse their complexities. Author Keywords
Uncovering, understanding, and predicting links

Network data, such as citation networks of documents, hyperlinked networks of web pages, and social networks of friends, are pervasive in applied statistics and machine learning. The statistical analysis of network data can provide both useful predictive models and descriptive statistics. Predictive models can point social network members towards new friends, scientific papers towards relevant citations, and web pages towards other related pages. Descriptive statistics can uncover the hidden community structure underlying a network data set. In this work we develop new models of network data that account for both links and attributes. We also develop the inferential and predictive tools around these models to make them widely applicable to large, real-world data sets. One such model, the Relational Topic Model can predict links using only a new node's attributes. Thus, we can suggest citations of newly written papers, predict the likely hyperlinks of a web page in development, or suggest friendships in a social network based only on a new user's profile of interests. Moreover, given a new node and its links, the model provides a predictive distribution of node attributes. This mechanism can be used to predict keywords from citations or a user's interests from his or her social connections. While explicit network data—network data in which the connections between people, places, genes, corporations, etc. are explicitly encoded—are already ubiquitous, most of these can only annotate connections in a limited fashion. Although relationships between entities are rich, it is impractical to manually devise complete characterizations of these relationships for every pair of entities on large, real-world corpora. To resolve this we present a probabilistic topic model to analyze text corpora and infer descriptions of its entities and of relationships between those entities. We show qualitatively and quantitatively that our model can construct and annotate graphs of relationships and make useful predictions.
Implementation of ADS Linked List Via Smart Pointers

Students traditionally have difficulties in implementing abstract data structures (ADS) in C++. To a large extent, these difficulties are due to language complexity in terms of memory management with raw pointers – the programmer must take care of too many details to provide reliable, efficient and secure implementation. Since all these technical details distract students from the essence of the studied algorithms, we decided to use in the course in DSA (Data Structures and Algorithms) an automated resource management, provided by the C++ standard ISO/IEC 14882:2011. In this work we share experience of using smart pointers to implement linked lists and discuss pedagogical aspects and effectiveness of the new classes, compared to the traditional library containers and implementation via built-in pointers.
Cache Replacement Policies for Improving LLC Performance in Multi-Core Processors

cache memory management can have adverse impact on the overall system performance. In a Chip Multi-Core (CMP) scenario, this effect can be enhanced as every core has a private cache apart from a larger shared cache. Replacement policy plays a key role in managing cache data. So it needs to be extremely efficient in order to extract the maximum potential of the cache memory. Over the years versatile set of replacement policies have been proposed and implemented and few of them (LRU, MRU etc) have proven to work well compared to others. However recent works have shown that few counter based replacement strategies have marginally outperformed LRU for certain workloads as LRU does not dynamically adapt to changing workload patterns. This work explores three counter based replacement techniques namely Context-Based Data Pattern Exploitation (CB-DPET), Logical Cache Partitioning Technique (LCP) and Sharing and Hit- Based Prioritizing Technique (SHP). Evaluation is carried out on 4 core and 8 core platforms (apart from 2 core platform which was already done as part of previous works) using PARSEC benchmarks and various performance metrics like throughput speedup, hit rate etc are captured and compared with that of LRU. All the three methods have produced better results on the performance metrics when compared to LRU.
Reshaping Data with the reshape Package

This paper presents the reshape package for R, which provides a common framework for many types of data reshaping and aggregation. It uses a paradigm of ‘melting’ and ‘casting’, where the data are ‘melted’ into a form which distinguishes measured and identifying variables, and then ‘cast’ into a new shape, whether it be a data frame, list, or high dimensional array. The paper includes an introduction to the conceptual framework, practical advice for melting and casting, and a case study.
Design of Query Suggestion System using Search Logs and Query Semantics

Query suggestion is an assistive technology mechanism commonly used in search engines to enable a user to formulate their search queries. Query suggestion extends the feature of search engine which helps users to describe their information needs more clearly. Building an efficient query suggestion system, however, is very difficult due to the fundamental challenge of predicting users’ search intent, especially given the limited user context information. The proposed system based on both users’ query actual semantic meanings from online dictionary sites and learning from query logs which predicts user information needs. To achieve this, the first method uses the online dictionary resource to find the context of the query and second method mines the search logs using a similarity function to perform query clustering from search logs and provides the recommendations.
The Effects of Lee Silverman Voice Treatment Combined with Abdominal Respiration on Maximum Phonation Time in Parkinson ' s Disease

The Lee Silverman Voice Treatment (LSVT) is widely used as a treatment to enhance the coordination between the respiration and voice of patients with Parkinson’s disease (PD), in the language rehabilitation field. [1] LSVT is a method that is designed to enhance the loudness of patients and it has been proven to be effective for improving the speech intelligibility of patients with PD [2]. It is a systematic rehabilitation method that helps patients generate much stronger and louder voice than the patients could generate themselves [2]. Previous studies have reported that LSVT increased the loudness of patients with PD and was also effective in improving vocal cord vibration, voice quality, pitch inflection, breathing capacity, and swallowing function [3,4]. Recent studies have proposed that a rehabilitation program that integrates interventions related to respiration would be more effective in improving the functional activities of patients with neurological impairment such as stroke than a single-treatment program [5]. Nonetheless, there is not enough evidence on the effects of LSVT on the lung capacity and the coordination between the respiratory muscle and musculilaryngis of patients with PD. Moreover, only a few studies evaluated the effectiveness of comprehensive rehabilitation programs to enhance the respiratory capabilities of patients with PD. The objective of this study was to evaluate the effects of a respiratory rehabilitation program (four weeks of abdominal breathing training and LSVT) on the forced vital capacity, voice intensity, and maximum phonation time (MPT) of patients with PD accompanied by hypokinetic The Effects of Lee Silverman Voice Treatment Combined with Abdominal Respiration on Maximum Phonation Time in Parkinson's Disease
An Appraisal of Meta-Heuristic Resource Allocation Techniques for IaaS Cloud

Background/Objectives: This appraisal investigates the meta-heuristics resource allocation techniques for maximizing financial gains and minimizing the financial expenses of cloud users for IaaS in cloud computing environment. Methods/Statistical Analysis: Overall, a total of ninety-one studies from 1954 to 2015 have been reviewed in this paper. However, twenty-three studies are selected that focused on the meta-heuristic algorithms for their research. The selected papers are categorized into eight groups according to the optimization algorithms used. Findings: From the analytical study, we pointed out the various issues addressed (optimal and dynamically resource allocation, energy and QoS aware resource allocation, VM allocation and placement) through resource allocation meta-heuristics algorithms.Whereas, the improvement shows better performance concerns minimizing the execution and response time, energy consumption and cost while enhancing the efficiency and QoS in this environment. The comparison parameters (makespan 35%,execution time 13%, response time 26%, cost 22%, utilization21% and other 13% including energy, throughput etc) and also the experimental tools (CloudSim 43%, GridSim 5%, Simjava 9%, Matlab 9% and others 13%) used for evaluation of the various techniques for resource allocation in IaaS cloud computing. Applications/Improvements: The comprehensive review and systematic comparison of meta-heuristic resource allocation algorithms described in this appraisal will help researchers to analyze different techniques for future research directions.
MATRIX: MANY-TASK COMPUTING EXECUTION FABRIC FOR EXTREME SCALES

iii ACKNOWLEDGEMENT It gives me a great pleasure to thank all those people who have helped me and contributed for the successful completion of this work. First of all, I would like to thank my advisor Dr. Ioan Raicu for giving me the opportunity to work under him on this project. The course I took under him triggered my passion in the field of distributed systems. His constant support and encouragement throughout my two years of graduate school has made my thesis possible. I also thank him for his patience, expertise and allowing me to work independently which eased me throughout my thesis. I would like to thank my parents Mr. Rajendran and Mrs. Sowdhamini Rajendran as well as my sister Mrs. Sheetal Balajee who have constantly supported me in boosting my morale. I dedicate this work to them. I would also to thank Dr. Lan and Dr. Sun for everything they taught me about Distributed Systems through their invaluable courses that broadened my knowledge. I want to thank Dr. Lan for being a member of the thesis examining committee.
Developing an MCS index using the climatology of South America

Even though mesoscale convective systems (MCS) are of great importance in precipitation regimes besides being related to severe weather events, they are still not easy to predict. This study builds an objective index for South American MCS based on synoptic features present before the initiation of the MCS between 2005 and 2010. The National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) product is used to access environmental conditions near the MCS‐initiation centroid and in unorganized convective environments to obtain a South American MCS index (SA‐MCS index). Upper and middle atmospheric levels presented the main patterns used to identify the MCS environments and unorganized convection, with the jet streak downstream of a short trough at medium levels frequently present in the convectively organized environment. The SA‐MCS index is shown to depend on a vertical wind shear between 0 and 6 km high, temperature advection at 775 hPa, lifted index (LI) and vertical velocity omega at 800 hPa. When compared with the MCS index developed through the United States MCS climatology, the SA‐MCS index was able to predict more than twice as many MCS at a distance of less than 1° from the point of maximum intensity of the index.
Controlly: Open source platform for learning and teaching control systems

The purpose of this paper is to expose an educative platform for the design of automatic control systems. Controlly is an open source platform designed for teaching and learning of control systems (SISO and MIMO) based on Google interface Blockly. This interface provides the design of programming algorithms through visual blocks which are built to generate the conversion into different classes of languages such as Python, Dart or Xml. The platform integrates hardware and software elements needed for pedagogical teaching of automatic control systems achieved a complementation between theoric and practical aspects. In this way, the student has more tools for reinforcing their learning process in this area. The purpose of the platform is focused in the construction of educational environment for learning, modular, and open source that provides all intuitive elements necessaries for the design of controllers through theoretical and empirical techniques commonly used in control design such as root locus, Ziegler Nichols, Cohen Coon or state space.
Miniature microcontroller-based heart rate telemeter processes single precordial lead

A miniature single-channel heart rate monitoring device has been developed specifically for use in critical care situations, such as intensive care units. The externally-worn device is placed directly on the chest above the heart, allowing acquisition of the electrocardiogram without the use of leads. The 8-bit microcontroller-based transmitter digitizes the preconditioned ECG and identifies the signal in real-time using a robust QRS detection algorithm. Data is output from the microcontroller only when an abnormal heart rate or low battery voltage is detected. When it is necessary to output data from the device, the digital data is transmitted by frequency modulation to a nearby nurses' station. At the monitoring station, the transmitted data is demodulated by the receiver and processed by another microcontroller which then activates the appropriate alarms. <<ETX>>
Rapid simulation of substrate coupling effects in mixed-mode ICs

Asymptotic waveform evaluation (AWE) techniques have been used to develop macromodels that allow accurate and efficient simulation of substrate-related parasitic electrical coupling effects using a modified version of SPICE3. While achieving comparable accuracy to a mixed device-circuit simulator (PISCES IIB), simulation time is reduced by several orders of magnitude. The macromodeling technique can be extended to simulate noise coupling in a VLSI chip, in contrast to conventional device-circuit simulators.
The Development of a Reliable and Valid Instrument to Assess Students ’ Attitudes towards Diversity , Violence , and their Perceived Need for Education on Diversity Adlane

Violations against heritage sites and minorities in the Middle East fuel the cycle of sectarian vengeance and violence(Ki-moon, 2014), and destroy the unity of human culture, the diversity and the cultural exchanges between cultural civilizations (Hamoudi, 2014). Consequently, if living in the midst of the fog of war is a daunting task (Bhabha, 2014), and if it is time to act (Dieng, 2014) , then educating Syrian and Iraqi refugees and Lebanese students to resent violence and embrace a diverse society is no more an option to think about, it is a necessity. Few measures exist to assess whether students embrace diversity, resent violence, and show a perceived need to be educated further on how to be responsible citizens. In this study, the researcher developed a valid and reliable instrument to explore diversity in the perspectives of Lebanese and Syrian students in Lebanon. The instrument, ―Students‘ Attitudes towards Diversity and Violence and their perceived need for Education on Diversity Evaluation Form‖ (SADVEEF), assessed three factors: (a) students‘ attitude towards diversity, (b) students‘ attitude towards violence, (c) students‘ perceived needs for education on diversity. The researcher gathered data from students of different ages, sex, races, ethnic groups, religions and religious sects. The instrument is evaluated, and how it might benefit education research and practice in the assessment of diversity related goals is considered. Using SADVEEF instrument will help draw the attention of policy makers that there is an urgent need to mold the curriculum to help cure and deal with the insecurities of refugee students and students living sectarian wars. ________________________________________________________________________________________