Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Single Document Automatic Summarization Method Based on Hybrid Neural Network

In order to extract the required information from the massive information, an automatic summarization method based on hybrid neural network is proposed to help people to browse and understand the document quickly, and improve the efficiency of automatic summarization. The method combines the Convolutional Neural Network that has high efficiency and small over-fitting phenomenon in the training process and the Long Short-Term Memory Neural Network model with good effect on sequence prediction. The model takes full account of the characteristics words, characteristic sentences, feature segment positions and other factors, and add a signal to the input of the Long Short-Term Memory. Experimental results show that compared with automatic summarization methods based on LSI model, LDA model, TextRank summarization algorithm, PCA summarization algorithm and Long Short-Term Memory Neural Network model, the proposed method based on hybrid neural network has a good effect on automatic summarization, and improves the quality of automatic summarization effectively.
Social Power in Interactions: Computational Analysis and Detection of Power Relations

Social Power in Interactions: Computational Analysis and Detection of Power Relations Vinodkumar Prabhakaran In this thesis, I investigate whether social power relations between individuals are manifested in the language and structure of their social interactions, and if so, in what ways, and whether we can use the insights gained from this study to build computational systems that can automatically identify these power relations. I analyze eleven different linguistic and/or structural aspects of interactions to study manifestations of power. To further understand these manifestations of power, I extend this study in two ways. First, I investigate whether a person’s gender and the gender makeup of an interaction (e.g., are most participants female? ) affect the manifestations of his/her power (or lack of it) and whether the gender information can help improve the predictive performance of an automatic power prediction system. Second, I investigate whether different types of power manifest differently in interactions, and whether they exhibit different but predictable patterns in the aspects of interactions we analyze. I perform this study on interactions from two different genres: organizational emails, which contain task oriented written interactions, and political debates, which contain discursive spoken interactions.
ROBUS: Fair Cache Allocation for Multi-tenant Data-parallel Workloads

Systems for processing big data---e.g., Hadoop, Spark, and massively parallel databases---need to run workloads on behalf of multiple tenants simultaneously. The abundant disk-based storage in these systems is usually complemented by a smaller, but much faster, {\em cache}. Cache is a precious resource: Tenants who get to use cache can see two orders of magnitude performance improvement. Cache is also a limited and hence shared resource: Unlike a resource like a CPU core which can be used by only one tenant at a time, a cached data item can be accessed by multiple tenants at the same time. Cache, therefore, has to be shared by a multi-tenancy-aware policy across tenants, each having a unique set of priorities and workload characteristics. In this paper, we develop cache allocation strategies that speed up the overall workload while being {\em fair} to each tenant. We build a novel fairness model targeted at the shared resource setting that incorporates not only the more standard concepts of Pareto-efficiency and sharing incentive, but also define envy freeness via the notion of {\em core} from cooperative game theory. Our cache management platform, ROBUS, uses randomization over small time batches, and we develop a proportionally fair allocation mechanism that satisfies the core property in expectation. We show that this algorithm and related fair algorithms can be approximated to arbitrary precision in polynomial time. We evaluate these algorithms on a ROBUS prototype implemented on Spark with RDD store used as cache. Our evaluation on a synthetically generated industry-standard workload shows that our algorithms provide a speedup close to performance optimal algorithms while guaranteeing fairness across tenants.
A Novel Design of a Multi-layer 2:4 Decoder using Quantum- Dot Cellular Automata

(Received 26 Dec. 2018; Revised 21 Jan. 2019; Accepted 20 Feb. 2019; Published 15 Mar. 2019) Abstract: The quantum-dot cellular automata (QCA) is considered as an alternative to complementary metal oxide semiconductor (CMOS) technology based on physical phenomena like Coulomb interaction to overcome the physical limitations of this technology. The decoder is one of the important components in digital circuits, which can be used in more comprehensive circuits such as full adders and memories. This paper proposes the 2:4 decoder using multilayer QCA technology. The multilayer decoder provides requirements of high-density devices with good computing power. The proposed 2:4 active high decoder reduces the clock phases to three, making the circuit faster than previous ones. Moreover, the number of cells and occupied space are significantly reduced, thus reducing power consumption. Multi-layer architecture has more reliability than coplanar architecture and allows for the easy layout change. The structure was simulated using QCA Designer software, and the simplified expressions and standard functions have been presented. The simulation results can be useful for using them in logic circuits.
WSN Using Collective Scheme of Energy Efï¬(cid:129)cient Grouping and Evidence Retrieval

The two-layer network structure has been widely adopted in wireless sensor networks (WSNs) for managing sensor nodes. In such a structure, the low layer nodes communicate with their cluster head, followed by the cluster-head nodes communicating with the base station operating in either a one hop or a multi-hop manner. The main focus of node-clustering algorithms is minimizing energy consumption due to strictly limited resources in WSNs. Also, WSNs are data intensive networks with the capability of providing users with accurate data. Unfortunately, data missing is common in WSNs. In this paper, we propose a novel joint design of sensor nodes clustering and data recovery, where the WSNs is organized in a two-layer manner with our developed clustering algorithm, and then the missing data is recovered based on this two-layer structure. Furthermore, in the proposed clustering algorithm, we take both the energy-efﬁciency and data forecasting accuracy into consideration and investigate the tradeoff between them. This is based on the key observation that the high energy-efﬁciency of the network can be achieved by reducing the distances among the nodes in a cluster, while the accuracy of the forecasting results can be improved by increasing the correlation of the data stream among the nodes in a cluster. Simulation results demonstrate that our joint design outperforms the existing algorithms in terms of energy consumption and forecasting accuracy.
Generalized Hamacher Aggregation Operators for Intuitionistic Uncertain Linguistic Sets: Multiple Attribute Group Decision Making Methods

In this paper, we consider multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of intuitionistic uncertain linguistic variables. Based on Hamacher operations, we developed several Hamacher aggregation operators, which generalize the arithmetic aggregation operators and geometric aggregation operators, and extend the algebraic aggregation operators and Einstein aggregation operators. A number of special cases for the two operators with respect to the parameters are discussed in detail. Also, we developed an intuitionistic uncertain linguistic generalized Hamacher hybrid weighted average operator to reflect the importance degrees of both the given intuitionistic uncertain linguistic variables and their ordered positions. Based on the generalized Hamacher aggregation operator, we propose a method for MAGDM for intuitionistic uncertain linguistic sets. Finally, a numerical example and comparative analysis with related decision making methods are provided to illustrate the practicality and feasibility of the proposed method.
Semantic Interior Mapology: A Toolbox For Indoor Scene Description From Architectural Floor Plans

Author(s): Trinh, Viet; Manduchi, Roberto | Abstract: We introduce the Semantic Interior Mapology (SIM) toolbox for the conversion of a floor plan and its room contents (such as furnitures) to a vectorized form. The toolbox is composed of the Map Conversion toolkit and the Map Population toolkit. The Map Conversion toolkit allows one to quickly trace the layout of a floor plan, and to generate a GeoJSON file that can be rendered in 3D using web applications such as Mapbox. The Map Population toolkit takes the 3D scan of a room in the building (acquired from an RGB-D camera), and, through a semi-automatic process, populates individual objects of interest with a correct dimension and position in the GeoJSON representation of the building. SIM is easy to use and produces accurate results even in the case of complex building layouts.
Policies Based Privacy Control Mechanisms for Social

We introduce two approaches for improving privacy policy management in online social networks. First, we introduce a mechanism using proven clustering techniques that assists users in grouping their friends for group based policy management approaches. Second, we introduce a policy management approach that leverages a user's memory and opinion of their friends to set policies for other similar friends. We refer to this new approach as Same-As Policy Management. To demonstrate the effectiveness of our policy management improvements, we implemented a prototype Face book application and conducted an extensive user study. Leveraging proven clustering techniques, we demonstrated a 23% reduction in friend grouping time. In addition, we demonstrated considerable reductions in policy authoring time using SameAs Policy Management over traditional group based policy management approaches. Finally, we presented user perceptions of both improvements, which are very encouraging.
Efficient approaches for functional validation of soc designs using high-level specifications

Increasing complexity coupled with time-to-market pressure create a critical need to raise the abstraction level for System-on-Chip (SoC) designs. Functional validation is widely acknowledged as a major bottleneck due to lack of automated techniques and limited reuse of validation efforts between abstraction levels. Simulation is the most widely used form of validation using random or constrained-random tests. Directed tests are very promising for simulation since only fewer directed tests are required compared to billions of random tests to achieve a coverage goal. Currently, directed test generation is performed manually which is time-consuming and error-prone. This dissertation presents a novel top-down methodology for automatically generating directed tests from high-level specifications and reuse them across different abstraction levels. The objective is to reduce the overall functional validation effort. My research has four major contributions: i) it proposes a method that can extract formal models from high-level SoC specifications; ii) it presents an approach that can automatically derive properties based on fault models; iii) it proposes efficient clustering, learning and decomposition techniques to reduce the directed test generation time; and iv) it provides validation refinement approaches to enable reuse of the system-level validation efforts for low-level implementation validation as well as to check the consistency between different abstraction layers. Our experimental results using both software and hardware benchmarks demonstrate that the proposed approaches can significantly reduce the overall validation effort.
Efficient Algorithms for Fitting Bayesian Mixture Models

Mixture distributions have been given considerable attention due to their flexible form and convenience of use. Markov Chain Monte Carlo (MCMC) methods enable us to generate samples from a target distribution from which it is difficult to sample directly by simulating a Markov chain. However, practical difficulties arise when MCMC methods are implemented to fit mixture distributions with several isolated modes. Most MCMC sampling methods have difficulties transitioning between the isolated modal regions and the inferences based on the samples generated by these methods can be unreliable. This motivated us to develop efficient algorithms for fitting Bayesian mixture models. Our approach hinges on the premise that a preliminary understanding of some essential features of the posterior distribution is needed to make sampling more efficient. In this thesis we introduce two algorithms that rely on an initial identification of possible isolated modes of the mixture distribution. The algorithms are applied to fit four different models: a Bayesian univariate normal mixture model; a Bayesian univariate outlier accommodation model; a Bayesian linear regression model; and a hierarchical Bayesian regression model for repeated measures data. Their performance is compared to that of other methods including the Gibbs sampler and an MCMC tempering transition method by examining the accuracy of inferences and the ease of transition between isolated modal regions of the posterior distributions for the ii Bayesian models. The results show that the proposed algorithms outperform the Gibbs sampler and the tempering transition method.
Value Orientation Scale: The Validation Of The Pancasila Scale

In previous research (Meinarno & Suwartono, 2011), we developed 32 items from five values of Pancasila, however the psychometric properties of the scale are yet to be explored. In this reasearch, we identfied five dimensions of the scale, namely spirituality, humanity, nationality, democracy, and social justice. Using convenience sampling, with Webropol survey software to collect the data, of 234 participants, 59% were female, with range of age 15 35 years old (M=18.42, SD = 1.74). In this research, we focused on construct validation of a measurement model using confirmatory factor analysis. We found that the Pancasila scale consists of 25 items. The Cronbach's alpha coefficients for each dimension were 0.78, 0.70, 0.79, 0.70, and 0.77 respectively. Then, we explored the underlying factor structure and found five latent value factors and these were spirituality, humanity, nationality, democracy, and social justice. The five factors,each formed a unidimensional scale. However, these five latent factors cannot be explained by the high order of the latent factor, namely Pancasila. The second order factorial structure using confirmatory factor analysis did not fit. The fit indices showed that the model did not fit the data well. The results of CFA did not confirm the presence of a second-order factorial structure of Pancasila.
APLIKASI PENDEKATAN FUZZY DELPHI UNTUK MEMBANGUNKAN PEMBELAJARAN KANAK-KANAK HOMESCHOOLING MENGGUNAKAN MEDIA ANIMASI INTERAKTIF,

Abstrak: Ledakan teknologi maklumat telah mencetuskan fenomena baru dalam pengajaran dan pembelajaran khususnya kepada pendidikan alternatif homeschooling. Animasi Interaktif merupakan pendekatan baharu yang boleh dijadikan medium pembelajaran bagi kanak-kanak homeschooling. Animasi interaktif merupakan media pendidikan yang mengintegrasikan lima elemen utama iaitu teks, grafik, audio, video dan animasi. Artikel ini bertujuan untuk mengenal pasti ciri-ciri animasi interaktif yang bersesuaian untuk dibangunkan sebagai medium pembelajaran bagi kanak-kanak homeschooling menggunakan pendekatan Fuzzy Delphi. Pendekatan pembelajaran kanak-kanak Homeschooling dengan berbantukan animasi interaktif bukan sahaja dapat memudahkan proses pembelajaran tetapi juga meningkatkan motivasi kanak-kanak homeschooling. Seramai 15 orang pakar bidang pendidikan kanak-kanak homeschooling dan teknologi pendidikan telah dipilih untuk menganalisis fuzziness konsensus pakar. Dapatan kajian menunjukkan bahawa animasi interaktif sesuai diaplikasikan sebagai medium pembelajaran bagi kanak-kanak homeschooling. Kata Kunci: Homeschooling, Home Education, Pendidikan Alternatif, Animasi Interaktif
Alternative Strategies to Explore the SNNB Algorithm Performance

Data mining is the process of extracting useful knowledge from large datasets. A subarea of data mining is the classification that induces a set of models for predicting the label of the unknown class. The Naive Bayes classifier is simple, efficient and robust; its performance has been improved by some works, which focused on finding an instances subset in a conditional way and selecting the appropriate classifier with the highest probability. In this paper we propose to modify the Selective Neighborhood based Naive Bayes (SNNB) algorithm, using and combining other distance measurements, instance organization, instance space search and model selection. The proposed combinations are aimed at exploring the classifying accuracy of the SNNB algorithm. Experimental results show that the best strategy found (using 26 datasets from the UCI repository) won in 15 cases and only lost in 3 cases
Well-being from an occupational perspective : testing a conceptual model

One of the key factors for promoting well-being lies in balancing one's daily life occupations and the nature of these occupations. Yet it is not clear what constitutes occupational balance, and its association to other factors has not been examined systematically. This dissertation proposed and tested a conceptual model for examining well-being from an occupational perspective using structural equation modeling. The proposed model stated the mediating role of occupational characteristics and occupational imbalance in the relationship between personality and well-being. Four studies were conducted in order to develop and test the model. The first three studies explored the measurement aspect of occupational balance and well-being, whereas the fourth study tested the overall model (the relationships among the model constructs). Method: 122 adults completed the Cross Impact Matrix (CIM) of the Personal Projects Analysis (PPA) to measure occupational balance; the Satisfaction With Life Scale (SWLS); the Positive Affect Negative Affect Scales, and the Self-Rated Health scale to measure well-being (study 1). As no correlation was found between well-being and occupational balance, measured by the CIM, a pilot sample (n=24) completed the same instruments, except occupational balance was measured using an alternative tool, the Inter-goal Relations Questionnaire (IRQ). Results indicated that occupational balance and occupational imbalance were two distinct dimensions that should be measured separately using unipolar scales. This led to additional studies while examining occupational imbalance using the IRQ and focusing on one aspect of well-being (life satisfaction). 288 adults completed the IRQ (occupational imbalance), the PPA Rating Matrix (occupational characteristics), the Big Five Inventory (personality traits) and the SWLS (well-being). Results supported the unidimensional structure of occupational imbalance (study 2) and well-being (study 3); whereas, the overall tested models were partially confirmed (study 4). Occupational characteristics served as significant mediators between personality and well-being, yet occupational imbalance did not. Conclusion: the quality of occupations is what is important to well-being, rather than the way individuals balance them. These findings are in line with very recent and innovative theories for viewing balance. New lines of inquiry
Statistical Analysis and Predicting Kidney Diseases using Machine Learning Algorithms

: Data mining techniques has been used as a recent trend for achieving diagnostics results, in medical fields such as kidney dialysis. Data mining concepts are used to examine a rich collection of data from different perspectives and deriving useful information. This project intends to diagnosis and prediction system based on predictive mining. This paper covers the essential problems associated with the data mining method and its usage within the medical field. The data has been collected from Visakhapatnam district during 2014 and 2015 with 690 instances and 49 attributes. This has been done in cases of kidney diseases. It was proved that the data mining is applicable within the medical sector and can improve the various medical applications . The K-Means(KM) algorithm is a major role in determine the number of clusters k for large datasets The data mining classification techniques are done based on namely ADT trees, Naïve Bayes , J48 are analyzed on kidney disease data set.
AUTOMATIC ROAD MARKINGS EXTRACTION, CLASSIFICATION AND VECTORIZATION FROM MOBILE LASER SCANNING DATA

Abstract. To meet the demands of various applications such as high definition navigation map production for unmanned vehicles and road reconstruction and expansion engineering, this paper proposes an effective and efficient approach to automatically extract, classify and vectorize road markings from Mobile Laser Scanning (MLS) point clouds. Firstly, the MLS point cloud is segmented to ground and non-ground points. Secondly, several geo-reference images are generated and further used to detect road markings pixels under an image processing scheme. Thirdly, road marking point clouds are retrieved from the image and further segmented into connected objects. Otsu thresholding and Statistic Outlier Remover are adopted to refine the road marking objects. Next, each road marking objects are classified into several categories such as boundary lines, rectangle road markings, etc. based on its bounding box information. Other irregular road markings are classified by a model matching scheme. Finally, all classified road markings are vectorized as closed or unclosed polylines after reconnecting the breaking boundary lines. Comprehensive experiments are done on various MLS point clouds of both the urban and highway scenarios, which show that the precision and recall of the proposed method is higher than 95% for road marking extraction and as high as 93% for road marking classification on highway scenarios. The ratio is 92% and 85% for urban scenarios.