Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Specific I/O Scheduling Framework for Cloud Storage

Virtualization is one of the important enabling technologies for Cloud Computing which facilitates sharing of resources among the virtual machines. However, it incurs performance overheads due to contention of physical devices such as disk and network bandwidth. Various I/O applications having different latency requirements may be executing concurrently on different virtual machines provisioned on a single server in Cloud data-centers. It is pertinent that the performance SLAs of such applications are satisfied through intelligent scheduling and allocation of disk resources. The underlying disk scheduler at the server is unable to distinguish between the application requests being oblivious to the characteristics of these applications. Therefore, all the applications are provided best effort services by default. This may lead to performance degradation for the latency sensitive applications. In this work, we propose a novel disk scheduling framework PriDyn (Dynamic Priority) which provides differentiated services to various I/O applications co-located on a single host based on their latency attributes and desired performance. The framework employs a scheduling algorithm which dynamically computes latency estimates for all concurrent I/O applications for a given system state. Based on these, an appropriate priority assignment for the applications is determined which is taken into consideration by the underlying disk scheduler at the host while scheduling the I/O applications on the physical disk. The proposed scheduling framework is able to successfully satisfy QoS requirements for the concurrent I/O applications within system constraints. This has been verified through extensive experimental analysis. In order to realize the benefits of differentiated services provided by the PriDyn scheduler, proper combination of I/O applications must be ensured for the servers through intelligent meta-scheduling techniques at the Cloud data-center level. For achieving this, in the second part of this work, we extended the PriDyn framework to design a proactive admission control and scheduling framework PCOS (Prescient C loud I/O Scheduler). It aims to maximize the
Ontology-based Architecture for an Arabic Semantic Search Engine

Abstract Most of the current Arabic search engines are classified as syntactic search engines, since the search is based on keyword(s). These search engines present several problems related to the meaning of the search query. For example, the low query precision and the shortness in understanding user's query intention represent some of these problems. In this paper, an Arabic semantic search engine based on an Arabic ontology. The proposed architecture is layered, and is loosely coupled with an existing Arabic syntactic search engine. The proposed Arabic semantic search engine is semantically reason using an Arabic ontology that represents a very rich vocabulary (Arabic concepts' attributes, inheritance relations, and association relations). It helps the search engine to understand the user's query intention, and hence enhances the search results. Finally, the paper illustrates semantic search through simple search examples in computer domain. 1. I NTRODUCTION
Phoneme vs. Diphone in Unit Selection TTS of Lithuanian

The present paper deals with choosing the base type for the unit selection speech synthesis method of the Lithuanian language. Phoneme and diphone units have been examined. Besides, two different methods of joining costs calculation were employed in a diphone synthesizer: one was based on the spectral similarity and the other was based on phonological classes of the sounds to be joined. Synthesizers were evaluated according to their performance, algorithm complexity, the number of joins in a synthesized speech and the human listeners’ subjective judgment. Experimental testing showed that the diphone synthesizer based on phonological classes was much more acceptable to the listeners than the one based on the spectral similarity. The diphone synthesizer based on phonological classes outperformed the phonemic synthesizer in terms of performance and the number of joins though it was somewhat less acceptable to human listeners.
Opinion extraction and classification of real time Facebook Status

Social media like Facebook today are not only just a website. They are now become much popular communication tool for internet users. It is a medium through which users belonging to any of category, profession can make their comments. These all comments have contained some features along with it. These comments or status are really useful which are actually viewed as their ‘OPINIONS’. Opinions are really important while we need to analyze any of product, topic, discussion and whatever which will require some user opinions to draw some inferences and conclusions from them. Social media plays an important role for this intention. In this paper we focused on facebook statuses, which we can view as opinions of users or their reaction on concern we want to analyze. We develop tool status puller that automatically collects random facebook statuses. Then we make classifier that performs classifications on that corpus collected from facebook. Our classifier is able to extract three features GOOD, BAD and AVERGAE from that statuses respectively. As per classifier results we perform evaluations experiments which further can be work for feature mining of user opinions on facebook. It’s pure new and unique technique proposed in the field of opinion mining.
Doctor Patient Interaction System for Android

The establishment and improvement of doctor-patient interaction system is a very important requirement, especially when the communication technology is developing rapidly. The advantages of the web can be l useful to make up the time and distance between doctors and patients and to provide fast and adequate medical services. Through the connection between user terminals and specific service, both doctors and patients are able to obtain required data to achieve a better interaction. The platform, Web services, and database technology are all gradually maturing so that we can develop a doctor- patient interaction system for Android to meet the needs of the patient and to provide Communication with patients by the doctor's more efficient and convenient means of communication with patients.
Bearing the Weight of Imprisonment: The Relationship Between Prison Climate and Well-Being

Little is known about the relative influence of shared and individual perceptions of prison climate on adjustment to incarceration. This study investigated the relationship between prison climate and well-being among a sample of 4,538 adults incarcerated in the Netherlands. Prison climate dimensions were considered both as prison unit-level variables and as individual-level perceptions. Multilevel analysis results showed that most variance for well-being is found at the individual rather than the unit level. This implies that it does not make much of a difference for well-being in which prison unit someone resides. Positive effects of prison climate on well-being were primarily found for individual perceptions of prison climate, rather than for the aggregate unit measures. More research is needed to determine whether this finding holds true in other countries. The findings confirm the importance of disentangling the contribution of prison climate at the individual and group level.
‘I feel like I’m getting sad there’: early childhood outdoor playspaces as places for children’s wellbeing

ABSTRACT Children’s physical activity has long been the primary focus of outdoor playspace design. Other influences are adult views on risk and economic considerations. However, a growing body of research shows young children’s perspectives on their outdoor playspaces need to be accounted for. Positioned within a mixed-method comparative case study, this paper showcases the stories that young children have told during participatory research about two different early childhood outdoor playspaces. Recurrent wellbeing themes emerged highlighting children’s agency, place attachment and the need to hide. Analysis of the children’s stories indicated that a spacious open-ended environment, with significant natural features, selected built equipment and secluded spaces, fostered children’s experiences of wellbeing through play. Overcrowding, and a preponderance of artificial surfaces and materials appeared to work against children’s wellbeing. The findings indicate that good outdoor playspace design acknowledges the importance of place in children’s lives, and involves them in co-construction of their playspaces. GRAPHICAL ABSTRACT
Embodied Simulation. Its Bearing on Aesthetic Experience and the Dialogue Between Neuroscience and the Humanities

Summary Embodied simulation, a basic functional mechanism of our brain, and its neural underpinnings are discussed and connected to intersubjectivity and the reception of human cultural artefacts, like visual arts and film. Embodied simulation provides a unified account of both non-verbal and verbal aspects of interpersonal relations that likely play an important role in shaping not only the self and his/her relation to others, but also shared cultural practices. Embodied simulation sheds new light on aesthetic experience and is proposed as a key element for the dialogue between neuroscience and the humanities within the biocultural paradigm.
Topic regression

Text documents are generally accompanied by non-textual information, such as authors, dates, publication sources, and, increasingly, automatically recognized named entities. Work in text analysis has often involved predicting these non-text values based on text data for tasks such as document classification and author identification. This thesis considers the opposite problem: predicting the textual content of documents based on non-text data. In this work I study several regression-based methods for estimating the influence of specific metadata elements in determining the content of text documents. Such topic regression methods allow users of document collections to test hypotheses about the underlying environments that produced those documents.
Quantization and training of object detection networks with low-precision weights and activations

Abstract. As convolutional neural networks have demonstrated state-of-the-art performance in object recognition and detection, there is a growing need for deploying these systems on resource-constrained mobile platforms. However, the computational burden and energy consumption of inference for these networks are significantly higher than what most low-power devices can afford. To address these limitations, this paper proposes a method to train object detection networks with low-precision weights and activations. The probability density functions of weights and activations of each layer are first directly estimated using piecewise Gaussian models. Then, the optimal quantization intervals and step sizes for each convolution layer are adaptively determined according to the distribution of weights and activations. As the most computationally expensive convolutions can be replaced by effective fixed point operations, the proposed method can drastically reduce computation complexity and memory footprint. Performing on the tiny you only look once (YOLO) and YOLO architectures, the proposed method achieves comparable accuracy to their 32-bit counterparts. As an illustration, the proposed 4-bit and 8-bit quantized versions of the YOLO model achieve a mean average precision of 62.6% and 63.9%, respectively, on the Pascal visual object classes 2012 test dataset. The mAP of the 32-bit full-precision baseline model is 64.0%.
Robust and incremental active contour models for object tracking

This paper addresses object tracking problems in an image sequence using an active contour model called '(rho) -snake'. This model is the result of the combination of classical snakes and elements from the robust estimators theory. Snakes are energy-minimizing curves with global constraints that segment deforming shapes. The theory of robust estimators provides a framework that makes parameter estimation free from outliers. We have introduced (rho) - snakes to use these two techniques to achieve a goal: tracking a moving shape along an image sequence without being influenced by erroneous information of images. Attempting to imporve this new technique, we present parallel processing and a faster way of implementing (rho) - snakes. We also have defined robust energies, both spatial and temporal. As these energies include prediction, they fit our problem: tracking poor contrasted and fast moving object in a noisy IR sequence.
Hastening Write Operations on Read-Optimized Out-of-Core Column-Store Databases Utilizing Timestamped Binary Association Tables

The purpose of this thesis is to extend previous research on Out-of-Core column-store databases. Following use of the Asynchronous Out-of-Core update, which kept track of data using timestamps, an appendix is created which holds the newest timestamps and updated data by appending entries to the tables as new tuples. The appendix is naturally unsorted and unindexed by nature, causing need for a linear search that is not only slow, but causes ever-increasing query time as the volume of data within the appendix expands. Although measures exist to merge the appendix with the original body of the data, which is sorted and indexed, it only makes searching on the data swifter once the merging of tuples is complete. For this reason, the use of an oﬀset B-Tree index to allow for more eﬃcient searches on the appendix is proposed.
Dysfunctional personality traits: relationship with Five Factor Model, adaptation and symptomatology in a community sample from Buenos Aires

The paper introduces an analysis of the dimensional maladaptive personality traits model stated in the section III of Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), conducted on a community sample composed of 906 adults from Buenos Aires, Argentina. Data were gathered using a socio-demographic survey as well as the Personality Inventory for DSM-5 (PID-5), the Big Five Inventory (BFI), the Symptom Check List 90-R (SCL-90-R), and the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0). A joint exploratory factor analysis was carried out including the PID-5’s 25 facets along with BFI’s 5 factors as input variables. Findings showed a 5-factor structure mostly coincident with DSM-5’s hypotheses as well as with previous research. Besides, correlations calculated between PID-5 and BFI scores behaved according to theoretical hypotheses. By means of a two-stage cluster analysis which used WHODAS 2.0 score and the Global Severity Index index from SCL-90-R as segmentation criteria, two groups were differentiated: The High-Adaptation/Low-Symptomatology group vs the Low- Adaptation/High-Symptomatology one. The second group obtained significantly higher means in the five domains, and in 24 of the 25 facets of PID-5.
Perceived Organizational Support and Organizational Identification: The Mediating Role of Fit to Organization

The aim of this study is to reveal the effect of perceived organizational support (POS) on organizational identification (OI) and to test the mediating role of fit to organization (FO) within the context of academicians. Data are collected from 193 academicians by questionnaire. Correlation and Regression analyses are performed to test the research hypotheses. Results show that there is a strong correlation among variables and perceived organizational support is a significant contributor to organizational identification. Moreover, POS has an insignificant effect on OI, while FO significantly affects OI. With reference to Regression analysis, we also performed the Sobel test in order to determine the significance of the mediator effect. Sobel test results confirm that fit into organization of participants has a significant effect on OI; meanwhile, it is a mediator between organizational support perceived by participants and their organizational identification.
Future Community in Istanbul: An interpretation of Istanbul to generate a new urban life

The parametric design techniques are developed over the past 15 years. And a new style called parametricism is born, which is the style rooted in digital animation techniques. The parametricism is based on the advanced parametric design systems and scripting method (Schumacher, 2009). This study is the research of defining the city of Istanbul and the skyscraper together in the sense of the parametricism. The result is expected to be a self-sufficient urban living proposal by using generative and parametric tools and scripting techniques. The other purpose of this study is to examine the relationship between the skyscraper and the natural world, and the urban living.
Statistical Analysis of Complex Data: Bayesian Model Selection and Functional Data Depth.

Statistical Analysis of Complex Data: Bayesian Model Selection and Functional Data Depth by Naveen Naidu Narisetty Chairs: Xuming He and Vijayan N. Nair Big data of the modern era exhibit different types of complex structures. This dissertation addresses two important problems that arise in this context. Consider highdimensional data where the number of variables is much larger than the sample size. For model selection in a Bayesian framework, a novel approach using sample size dependent spike and slab priors is proposed. It is shown that the corresponding posterior has strong variable selection consistency even when the number of covariates grows nearly exponentially with the sample size, and that the posterior induces shrinkage similar to the shrinkage due to the L0 penalty. A new computational algorithm for posterior computation is proposed, which is much more scalable in memory and in computational efficiency than existing Markov chain Monte Carlo algorithms. For the analysis of functional data, a new notion of data depth is devised which possesses desirable properties, and is especially well suited for obtaining central regions. In particular, the central regions achieve desired simultaneous coverage probability and are useful in a wide range of applications including boxplots and outlier detection for functional data, and simultaneous confidence bands in regression problems.
Self-healing approaches for the preventive repair of concrete structures: SARCOS COST Action

The SARCOS Action is focused on the preventive repair concept, with the objective of sealing small cracks at the earliest stage of damage, both for new and existing structures, and on looking for standardized methodologies to evaluate the mechanical and durability performance of the treated structures, with continuous feedback from the modelling of selfhealing mechanisms. The present contribution aims to give a general vision on the advances achieved within the SARCOS Action, including the revision of the state-of-the-art of the different aspects addressed within the Action: self-healing approaches, techniques for characterizing self-healing performance and self-healing mechanism modelling.
A Hybrid Question Answering System based on Ontology and Topic Modeling

A Question Answering (QA) system is an application which could provide accurate answer in response to the natural language questions. However, some QA systems have their weaknesses, especially for the QA system built based on Knowledge-based approach. It requires to pre-define various triple patterns in order to solve different question types. The ultimate goal of this paper is to propose an automated QA system using a hybrid approach, a combination of the knowledge-based and text-based approaches. Our approach only requires two SPARQLs to retrieve the candidate answers from the ontology without defining any question pattern, and then uses the Topic Model to find the most related candidate answers as the answers. We also investigate and evaluate different language models (unigram and bigram). Our results have shown that this proposed QA system is able to perform beyond the random baseline and solve up to 44 out of 80 questions with Mean Reciprocal Rank (MRR) of 38.73% using bigram LDA.
The Culture of Mee Bu for Declining Stunting: Aceh Local Wisdom in the Process of Reproduction

This study aims to find out the process of mee bu tujoh bulen as one of the cultures of the Acehnese community that seeks to meet the nutritional needs of pregnant and prevents stunting mothers. Qualitative research with a phenomenological approach is used to study the cultural phenomena of mee bu tujoh bulen from the views and experiences of the people. Respondents in this study were pregnant women, traditional birth attendants, traditional leaders and community leaders. Data processing is done by using data reduction analysis, data display, and conclusion drawing/verification techniques that are presented in narrative form. The results showed that the ingredients presented in mee bu culture consisted of bu kulah ( rice wrapped using young banana leaves which had been steamed and then rice wrapped in pyramid shape), side dishes consisting of grilled fish, beef, roasted chicken, cooked duck curries, eggs, and various types of fruit. In addition to those ingredients, the in-laws also prepared a set of betel (completed with ingredients such as areca nut, gambier, lime, cloves and tobacco or called bakong in local language). To sum up, the food brought to mee Bu Tujoh Bulen in cultural ceremony by parents-in-law to pregnant daughter-in-law contains good nutrients to support the fulfillment of nutrition for pregnant women therefore it can prevent and reduce the prevalence of stunting children.
Using Machine Learning to Uncover Latent Research Topics in Fishery Models

ABSTRACT Modeling has become the most commonly used method in fisheries science, with numerous types of models and approaches available today. The large variety of models and the overwhelming amount of scientific literature published yearly can make it difficult to effectively access and use the output of fisheries modeling publications. In particular, the underlying topic of an article cannot always be detected using keyword searches. As a consequence, identifying the developments and trends within fisheries modeling research can be challenging and time-consuming. This paper utilizes a machine learning algorithm to uncover hidden topics and subtopics from peer-reviewed fisheries modeling publications and identifies temporal trends using 22,236 full-text articles extracted from 13 top-tier fisheries journals from 1990 to 2016. Two modeling topics were discovered: estimation models (a topic that contains the idea of catch, effort, and abundance estimation) and stock assessment models (a topic on the assessment of the current state of a fishery and future projections of fish stock responses and management effects). The underlying modeling subtopics show a change in the research focus of modeling publications over the last 26 years.
The Impact of the Global Economic Crisis on Support for Democracy

This contribution provides a brief introduction into the second part of this HSR Special Issue. It informs about an ongoing project on the development of democracy in seven countries, five 'new' and two 'established' democracies. The five new democracies are Chile, South Korea, Poland, South Africa and Turkey. They are compared to Sweden and Germany. The five new democracies are located in different world regions and have different cultural and historical backgrounds. The contribution provides basic economic and political information on these countries. It also describes the common data base used by the six contributions: two surveys of members of parliament conducted in 2007 and 2013 as well as waves 5 and 6 of the World Values Survey conducted at about the same time. The common focus of all six contributions is the analysis of change in political legitimacy between 2007 and 2013.