Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Modeling and Multi-Objective Optimization for Data Analytics in the Cloud

Today’s cloud service providers guarantee machine availabilities in their Service Level Agreement (SLA), without any guarantees on performance measures according to a specific cost budget, and running analytics on big data systems require the user not to only reserve the suitable cloud instances over which the big data system will be running, but also setting many system parameters like the degree of parallelism and granularity of scheduling. Of course, these parameters, as well as the choice of the cloud instances need to meet user objectives regarding latency, throughput and cost measures, which is a complex task if it’s done manually by the user. Hence, the need to transform cloud service models from availabily to user performance objective rises and leads to the problem of multi-objective optimization. Given different cost models, the optimizer will search a multi-dimensional space, compute execution plans that are not dominated by others (known as Pareto plans) and explore meaningful tradeoffs between different objectives to find the optimal plan for each analytical task. It’s difficult to build a general purpose cost model for each new user objective because big data systems can run arbitrary analytics on heterogeneous hardware with very complex system behaviors. We consider analytical tasks encoded as dataflow programs as in Hadoop and Spark systems. When such dataflow programs are submitted to the cloud, we aim to provide a multi-objective optimizer that can automatically find an optimal execution plan of the dataflow program, which meets specific user performance objectives. Developing an optimizer for dataflow programs in the cloud raises two major challenges: The optimizer needs cost models for running complex dataflow programs in the cloud, and, it further needs a new algorithmic foundation for multi-objective optimization across user-specific objectives. In this project, we aim to develop a principled optimization framework that can take complex dataflow program and offer guarantees among multiple user objectives including throughput, latency and cost with some promising initial results reported in [11]. My contribution to this project consists of building a prediction model for one of the performance objectives (latency). So, I tried to explore what accuracy deep learning and other machine learning algorithms provide while learning such models from the system measurements collected from running and other workloads. This performance model will be used by the multi-objective optimizer to construct a pareto optimal skyline.
The effects of sample size on omics study: from the perspective of robustness and diagnostic accuracy

In the design of a specific omics study, sample size is an essential consideration that represents the balance among investigation ability, financial requirements and ethical issues. And it was reported critical to increase robustness and diagnostic accuracy. In this study, two metabolomics datasets were used to conduct analysis to understand the effects of sample size on omics study and strengthen the importance of it. Overlap and AUC were applied to measure the robustness and diagnostic accuracy respectively. The results showed the positive correlation between sample size and robustness, sample size and diagnostic accuracy. Moreover, the effects of sample size vary in datasets and indexes. So, for a specific dataset, sample size should be determined according to its purpose.
The Use of Instagram as a Mobile-Assisted Language Learning Tool

The present study sought to explore how English language learners (ELLs) used Instagram, a popular social networking site, for language learning purposes and to reveal their attitudes towards and experiences in using it as a mobile-assisted language learning (MALL) tool. Special interest was also placed on whether there were any distinct ELL profiles in using Instagram for independent and informal language learning. In this mixed-methods study, both quantitative and qualitative data were concurrently collected through an online comprehensive survey consisting of three sections. Ninety-seven Instagram using ELLs took part in this study. Basic descriptive statistics and a cluster analysis were conducted on the quantitative data, and a thematic analysis on the qualitative data. The results showed that Instagram has the potential to help ELLs to improve overall language skills in general, and vocabulary and communication skills in particular. Further, ELLs’ experiences in using Instagram as a MALL tool for informal language learning were largely positive. Additionally, two different language learner profiles (i.e., novice and experienced) emerged based on Instagram use habits and orientations. Overall, this study indicated that social networking platforms and MALL applications can be used as an effective mobile language learning tool.
06 Sentiment Analysis & Opinion Mining - Data Mining - Ingegneria e Scienze Informatiche, Cesena

introduzione a sentiment analysis & opinion mining: definizioni e problemi, metodi non supervisionati, opinion lexicon, analisi a livello di documento, frase, feature, frasi comparative, stato dell'arte. Deep learning per la sentiment classification & opinion mining, reti neurali, reti LSTM, GRU, word embeddings e relativa esercitazione in R.
A Novel Wireless Fast Charger Using Unregulated IPT Stage

The inductive power transfer (IPT) technique has many merits in battery charging compared to traditional charging methods. However, in low-power applications, most of the existing wireless chargers would employ redundant power conversion and isolation stages in order to be compatible with the wire-connected chargers, which leads to low overall charging efficiency. This paper proposes a novel wireless fast charger by using a two-stage configuration. An unregulated IPT stage can not only offer galvanic isolation but also provide high step-down conversion ratio, and it works like a transformer-based isolated converter. Then the whole charger only needs one more regulation stage to meet the output requirement. This paper focus on the IPT stage with high-input voltage and low-output voltage. The LCC-C compensation is selected for the voltage step-down purpose. In the analysis, the influence of layout size, resonance frequency, and the power level are discussed, based on which the circuit parameters are optimally designed. Finally, a 2MHz 25W IPT stage is used as an example to achieve 120V /12V conversion. The measured peak efficiency is 82 %, which is 9 % higher than the stage of the art product (5W, 5V/5V conversion).
A Survey on Odometry for Autonomous Navigation Systems

The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, self-contained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.
Design and Development of Automated Soil Quality Management System using LabVIEW

The efficient and profitable production of agricultural products depends on a suitable soil condition. Developing a wireless and Intranet-accessible system to monitor sensor readings and control actuators for determining the optimum soil conditions for agricultural activities to provide information for the farmer with the solutions to maintain the soil conditions is the most challenging work. Remote monitoring and control system (RMS) is easy to utilize and has good reliable facility, providing the efficient monitoring and maintenance of process. In this paper, automated soil quality management system is designed, which is a wireless and Intranet-accessible system using Wi-Fi. Remotely monitored and controlled robot monitors entire field condition in real time, by measuring soil parameters like soil EC (Electrical conductivity), soil pH, soil moisture and atmospheric humidity, even automated regulation of water supply using radio frequency (RF) technology, is provided based on soil moisture level and all this necessary information is provided to farmer at base station on LabVIEW (Laboratory virtual instrument engineering workbench) graphical user interface (GUI) and also through SMS using GSM. Proposed System is implemented using NI (National instruments) LabVIEW and NI myRIO (reconfigurable input output) which has built in Wi-Fi, with web servicing capability. Most useful advantage of the designed system is, it facilitates the re-configuration and re-programming ability remotely, in accordance with the change in environmental condition.
11 Text mining strategy for guideline development

Objectives The increasing rate of publications in medicine is a current concern. Find relevant and precise information with impact in patient care decision became complicated, making the process of relevant knowledge extraction difficult and time-consuming. In the guideline creation process the information overload synthesis is the core step in the process. In Latin America countries there is need for more efficient tools which enable the quality-based discovery, the extraction, the integration and the use of hidden knowledge in the publications, due to the limited trained personal. Text mining tools helps to cope with the interpretation of these large volume of data. To include the raw data in the publications and convert them to a format feasible of data mining could decreased the analysis time. This strategy should improve the actualization of the recommendations with instant review of the data. Method We conduct a pilot study comparation in a guideline develop (neonatal Sepsis) process for data extraction with the traditional strategy (using research algorithm, manual review and GRADE pro tool) versus Data mining strategy (PubMed Reminer, Trip database tool, GATE, Tabula 1.2.1, Meshy, Weka and free existing tools) and comparing resources, time (learning and implementing) and with the guideline recommendations as measurable results. Results There was a consistency in results during the evidence extraction process, but increased discrepancy in the data extraction between the traditional strategy and the text and data mining strategy (Kappa < 0.20) Conclusions Significant efforts are being made to personalize information retrieval and ranking process steps during a guideline creation. The use of text mining tools could reduce the costs and error in handling information. Nowadays the free tools are insufficiently precise in the extraction of text and data that does not replace the traditional method of conducting a clinical practice guide.
Wikipedia-based Corpora for Analyzing Revisions, Discussions and Text Quality in Collaborative Writing

Wikipedia is both a valuable resource and a remarkable example of a non-standard data source for computational linguistics research. It is a unique source of authentic corpus material to study collaborative writing in the Web and offers terabytes of collaboratively constructed content in numerous domains. Due to the magnitude, diversity and the collaborative construction of its content, creating task-specific corpora for computational linguistics from Wikipedia is a non-trivial task. For example, the semiand sometimes unstructured data makes its segmentation difficult. In this presentation, we report about our ongoing work on several Wikipedia-based corpora tailored to support computational linguistics research with respect to:
SEISMIC DETECTION OF SOLAR MESOGRANULAR-SCALE FLOW

Helioseismic correlation data computed from the Solar and Heliospheric Observatory/Michelson Doppler Imager high-resolution Doppler images were inverted for solar flows of horizontal scale between 5 and 45 Mm. The photospheric Doppler velocity inferred from the inversions was compared with the original Doppler images at each scale. For horizontal scales greater than about 15 Mm, the seismically inferred and directly observed flow maps look similar, as has been seen in studies of supergranular flow. At smaller scales, the similarity disappears, but regression analysis reveals significant correlation between the maps, demonstrating that solar p- and f-mode oscillations contain useful information about these flows. The slope of the computed regression plots is close to unity, indicating that mesogranular-scale flows extend at least a megameter below the photosphere.
Fluid flow queue models for fixed-mobile network evaluation

A methodology for fast and accurate end-to-end KPI, like throughput and delay, estimation is proposed based on the service-centric traffic flow analysis and the fluid flow queuing model named CURSA-SQ. Mobile network features, like shared medium and mobility, are considered defining the models to be taken into account such as the propagation models and the fluid flow scheduling model. The developed methodology provides accurate computation of these KPIs, while performing orders of magnitude faster than discrete event simulators like ns-3. Finally, this methodology combined to its capacity for performance estimation in MPLS networks enables its application for near real-time converged fixed-mobile networks operation as it is proven in three use case scenarios.
Sentiment Analysis on Mobile Phone Reviews Using Supervised Learning Techniques

— Opinion Mining or Sentiment Analysis is the process of mining emotions, attitudes, and opinions automatically from speech, text, and database sources through Natural Language Processing (NLP). Opinions can be given on anything. It may be a product, feature of a product or any sentiment view on a product. In this research, Mobile phone products reviews, fetched from Amazon.com, are mined to predict customer rating of the product based on its user reviews. This is performed by the sentiment classification of unlocked mobile reviews for the sake of opinion mining. Different opinion mining algorithms are used to identify the sentiments hidden in the reviews and comments for a specific unlocked mobile. Moreover, a performance analysis of Sentiment Classification algorithms is performed on the data set of mobile phone reviews. Results yields from this research provide the comparative analysis of eight different classifiers on the evaluation parameters of accuracy, recall, precision and F-measure. The Random Forest Classifiers offers more accurate predictions than others but LSTM and CNN also give better accuracy.
Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms

Image segmentation is an essential technique of image processing for analyzing an image by partitioning it into non-overlapped regions each region referring to a set of pixels. Image segmentation approaches can be divided into four categories. They are thresholding, edge detection, region extraction and clustering. Clustering techniques can be used for partitioning datasets into groups according to the homogeneity of data points. The present research work proposes two algorithms involving hybridization of K-Means ( KM ) and Fuzzy C-Means ( FCM ) techniques as an attempt to achieve better clustering results. Along with the proposed hybrid algorithms, the present work also experiments with the standard K-Means and FCM algorithms. All the algorithms are experimented on four images. CPU Time, clustering fitness and sum of squared errors (SSE) are computed for measuring clustering performance of the algorithms. In all the experiments it is observed that the proposed hybrid algorithm KMandFCM is consistently producing better clustering results.
Arabic Question Answering with Dialogue Support

Question Answering (QA) system is a combination of Information Retrieval(IR) and Natural Language Processing (NLP) techniques. It returns a specific answer in response to user question. However, a system that can interact with the user to clarify and refine the answer is required. We propose QA system that adopts a user model for adaptation and a dialogue interface for interaction with the user combined with information retrieval and natural language techniques for Arabic Language. Our system will be able to handle users’ questions in natural language and to present answers in in respect to the user’s preferences and expected needs. The system achieved a precision of 82.05% and a dialogue success rate of 71.6%. The result is highly promising. As an extension for the present work, we need to make the system more adaptive and capable to learn and evolve with every new interactive scenario.
Real time camera homography error modelling for sensorial data fusion

The camera homography estimation involves using complex algorithms. They provide good results after some interactions, however the error model provided for such algorithm seems to be no appropriate for sensorial data fusion in real time. In this work a new methodology is proposed for camera homography and IMU data fusion. The experiment consists in rotate the camera. The experiment is done for frames. Here it is presented results about the error in homography estimation with and without bias compensation. The last section concerns about the experimental results and conclusions about this approach.
Assessment of Temperature-Dependent Conductivity Effects on the Thermal Spreading/Constriction Resistance of Semiconductors

Thermal spreading resistance takes placewhenever heat flow leaves a source of finite area and enters awider area. The present study aims to obtain the thermal spreading/constriction resistance models of some semiconductor materials with temperature-dependent thermal conductivity. A steady-state nonlinear heat conduction equation of the problem is transformed into the Laplace equation via the Kirchhoff transformation. The concepts of individual isotropic half-space and heat-flux tube are used in this study. The spreading resistances of the semiconductor materials in arbitrary temperature andheat-flux ranges are compared to each other. Results indicate that spreading/ constriction resistance is affected by the temperature-dependent conductivity of semiconductor materials. The conductivity dependence of spreading/constriction resistance becomesmore significant as the difference between the temperatures of contact surface and convenient thermal sink becomes more evident.
Irregular Target Object Detection Based on Faster R-CNN

Forthe shortcomings of traditional target detection algorithms can only extract specific target features for detection, propose the Faster R-CNN target detecti-on model of deep learning, combined with VGG16 and ResNet101 convolutional neur-al network methods, to detection of irregular target objects. Experiments established two types of irregular target data sets, walnut and jujube, use the network training and testing, verified the feasibility of deep learning network for detecting irregular target objects. The experimental results show that the Faster R-CNN target detection networ-kof training on the self-built data set, the final detection result reaches 95%, which proves the effectiveness of the network for detecting irregular target objects.
Intelligent Agent Based Itinerary System for Route Optimizer and Tourist Guide

The demand for better transport and tourism development in Nigeria is far above the availabilities and keep increasing yearly. The policy on transport and Tourism in Nigeria consider developing the welfare of the nation in this regard and to booster the national economic of the Nation. T hus, the need for an effective router and standard tourist locator is vital demand in this sector of the economic to facilitate successful positive competition in the global market. The paper presents an intelligent agent based itinerary system for route optimizer and tourist guide via a synthesis of heuristic graph itinerary and adaptive routing algorithm. This research work aimed at developing a software application that propose possible path that lead to a destination using multi criterion approach optimizer that required two variables input (source and destination). We used a hybrid of graph technique and adaptive routing technique to optimize the best lest optimal pathway. The paper may eliminate the difficulties in interpreting road map on paper for traveler and enable travelers to make their journey within Nigeria without asking question.
Simulation, modelling and feedback control of the flow around a simplified square-back road vehicle

The present work investigates the use of wall-resolved Large Eddy Simulations (LES) to compute the flow around simplified square-back road vehicles. The objective being to simulate numerically the unsteady flow past such blunt bluff bodies and to assess the use of a linear feedback control technique for drag reduction. Wall-resolved large-eddy simulations are employed as a test-bed, but the control strategy could be transferrable to moving vehicle experiments as both the sensing (base pressure sensors) and the actuation (synthetic jets) are body-mounted. The control strategy attempts to reduce the pressure drag by acting on the wake rather than manipulating the flow separation location which is fixed for square-back road vehicles. This technique is known as direct wake control. The control technique exploits the link between wake flow fluctuations and mean drag reduction. The flow response to the synthetic jets is characterised using system identification, and controller design is via shaping the frequency reponse to achieve wake fluctuation attenuation. The first bluff body studied is an infinite spanwise blunt bluff body called D-shaped body. It exhibits two interacting shear layers and a large recirculation area. The designed controller successfully attenuates base pressure fluctuations, increasing the time-averaged pressure on the body base by 38%. The second bluff body is a three-dimensional simplified lorry in presence of a fixed floor. The wake flow is composed of four detached shear layers, which form the envelope of a large low-pressure recirculation area. For this case, unforced simulations captured numerically, for the first time, the wake bi-modality. Bi-modality manifests as a random displacement (switching) of the wake between preferred off-center locations. The unsteady flowfield is studied in great details using modal decomposition. High-frequency snapshots of the switching sequence allow us to propose an explanation for the triggering of the bi-modal switching. Finally, various feedback control configurations are assessed. Base pressure fluctuations are significantly reduced as targeted but it appears challenging to obtain a clear mean base pressure recovery. The best configuration yields to a mean base pressure increase of 2.1%. 4