Miracle-dz/UniFAR-FTUs huggingface dataset:
Planning with Incomplete User Preferences and Domain Models

Current work in planning assumes that user preferences and/or domain dynamics are completely specified in advance, and aims to search for a single solution plan to satisfy these. In many real world scenarios, however, providing a complete specification of user preferences and domain dynamics becomes a time-consuming and error-prone task. More often than not, a user may provide no knowledge or at best partial knowledge of her preferences with respect to a desired plan. Similarly, a domain writer may only be able to determine certain parts, not all, of the model of some actions in a domain. Such modeling issues requires new concepts on what a solution should be, and novel techniques in solving the problem. When user preferences are incomplete, rather than presenting a single plan, the planner must instead provide a set of plans containing one or more plans that are similar to the one that the user prefers. This research first proposes the usage of different measures to capture the quality of such plan sets. These are domain-independent distance measures based on plan elements if no knowledge of the user preferences is given, or the Integrated Preference Function measure in case incomplete knowledge of such preferences is provided. It then investigates various heuristic approaches to generate plan sets in accordance with these measures, and presents empirical results demonstrating the promise of the methods. The second part of this research addresses planning problems with incomplete domain models, specifically those annotated with possible preconditions and effects of actions. It formalizes the notion of plan robustness capturing the probability of success for plans during execution. A method of assessing plan robustness based on the weighted model counting approach is proposed. Two approaches for synthesizing robust plans are introduced. The first one compiles the robust plan synthesis problems to the conformant probabilistic planning problems. The second approximates the robustness measure with lower and upper bounds, incorporating them into a stochastic local search for estimating distance heuristic
A model based on software quality factors which predicts maintainability

Software quality metric were developed to measure the complexity of software systems. The authors relate the complexity of the system as measured by software metrics to the amount of maintenance necessary to that system. The authors have developed a model which uses several software quality metrics as parameters to predict maintenance activity. A description is given of three classifications of metrics that are used to measure the quality of source code: code metrics, which measure physical characteristics of the software, such as length or number of tokens; structure metrics, which measure the connectivity of the software, such as the flow of information through the program and flow of control; and hybrid metrics, which are a combination of code and structure metrics. <<ETX>>
Towards a model on tool use in computer-based learning environments

The Technology Acceptance Model (TAM) features perceived usefulness and perceived ease of use as core determinants influencing a system use. Similarly, the cognitive mediational paradigm emphasises learners' perceptions but focuses on learning settings. Based on these two frameworks, a tool use model for Computer-Based Learning Environments (CBLEs) was tested through two experimental conditions (N = 70 each) with no difference in prior knowledge. The conditions were embedded (forced tool use) and non-embedded (free tool use). Our model establishes self-efficacy as an external variable influencing tool use perceptions (perceived functionality and perceived usability) which in turn affect the quantity and quality of tool use and performance. Structural equation modelling analyses showed that self-efficacy was mediated by perceived usability which in turn influenced perceived functionality. Quantity of tool use and self-efficacy influenced performance significantly. Neither perceived functionality nor perceived usability had a significant impact on tool use.
FORMATION OF THE SYSTEM OF IMPLEMENTATION OF THE CRISIS MANAGEMENT MECHANISM BASED ON THE EXAMPLE OF MINING ENTERPRISES

In conditions of economic instability, the problem of implementation of the crisis management mechanism in modern industrial enterprises is of particular importance. The problem of the research is to develop recommendations as a result of the analysis of the financial condition of industrial enterprises of Ukraine in an unstable economy. The paper discusses the features of the implementation of anti-crisis programs in modern mining and processing enterprises with the determination of the share of equity for the difficult conditions of production and economic activity. The aim of the research is to analyze the financial condition of industrial companies and develop recommendations to overcome the crisis. Taking into account the specifics of mining and processing enterprises, in order to overcome the crisis and solve the problems of production for the effective use of raw materials, the paper identified logistics and financial opportunities, made a forecast of the financial condition in the near future and determined the probability of termination of their activities, as well as developed sound recommendations in the context of the implementation of the crisis management mechanism at iron ore plants. The main methods used in the study: analysis, synthesis, comparison, models for assessing the probability of bankruptcy.
Design and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data

ABSTRACT It remains difficult to develop a clear understanding of geo-located events and their relationships to one another, particularly when it comes to identifying patterns of events in less-structured textual sources, such as news feeds and social media streams. Here we present a geovisualization tool that can leverage computational methods, such as T-pattern analysis, for extracting patterns of interest from event data streams. Our system, STempo, includes coordinated-view geovisualization components designed to support visual exploration and analysis of event data, and patterns extracted from those data, in terms of time, geography, and content. Through a user evaluation, we explore the usability and utility of STempo for understanding patterns of recent political, social, economic, and military events in Syria.
Trust-based Top-k Item Recommendation in Social Networks ⋆

Collaborative filtering based methods have a low performance in the context of social recommendation due to the data sparsity issue and not considering the social network information that can be exploited to improve the performance. Trust-based methods attempt to reduce the data sparsity by utilizing the social network information. However, most of these methods are based on the explicit trust statements expressed by users, which are not available in the social networks such as Sina Weibo. In this paper, we present a trust metric to quantitatively measure the recommendation trust between pairs of users by aggregating the implicit trust and trust propagation values. We propose a trust-based latent factor model, which incorporates the pairwise recommendation trust values into the probabilistic model for top-k item recommendation. The experiments on Sina Weibo demonstrate that our method outperforms the collaborative filtering based methods and trust-based methods.
Autoregressive distributed lag cointegration analysis of youth unemployment in Kenya

In this paper we consider cointegration analysis in an autoregressive distributed lag (ARDL) structure. First, logarithmic transformation is performed on the series to reduce outlier effects and have elasticity interpreted in terms of percentage. Second, the variables are tested for stationarity using Augmented Dickey-Fuller test. Third, the Johansen Cointegration test is carried out to examine cointegration of the series. Fourth, cointegrated dynamic ARDL model is estimated using ordinary least squares (OLS) and effects of variables and their lags interpreted. The results indicate that Gross Domestic Product (GDP) and its two-year lag are the only ones having negative effect on youth unemployment, that is, one unit increase in GDP and GDP two-year lag reduce youth unemployment by 0.207922% and 0.2052705% respectively. Also, one unit increase in External Debt (ED) and ED two-year lag reduce youth unemployment by 0.07303% and 0.009116% respectively. Furthermore, unit increase in one-year lag of youth literacy rate is the only one which reduces youth unemployment by 0.0892691%; one-year and three-year lag of population (POP) reduce youth unemployment by 0.2590455% and 4.3093119% respectively. The Foreign Direct Investment (FDI) and Private Investment (PI) do not have significant effects on youth unemployment. In the long run, increase in GDP causes increase in youth unemployment by 0.09148447%. The long run result explains that GDP growth in the country is “jobless growth” mainly in less labour intensive sectors.
Increasing DHT Data Security by Scattering Data

This paper describes methods for increasing the security of data being stored in a distributed hash table (DHT) which leverages the inherent properties of the DHT to provide a secure storage substrate. The methods presented are based upon a framework referred to as “Scatter, Conceal, and Recover” (SCAR). The standard method of securing data in a DHT is to encrypt the data using symmetrical encryption before storing it in the network. SCAR provides this level of security, but also prevents any known cryptoanalisys from being performed. It does this by dividing data into multiple blocks and scattering these blocks within the DHT. The security of SCAR is provided by the property that an attacker is unable to obtain and reassemble the data blocks correctly. However, if the attacker has access to the network communication, the likelihood of a successful attack is significantly increased. This paper defines how such attacks can be executed and provides methods for ensuring data security in spite of such attacks.
Airfoil shape optimization by coupling computational fluid dynamics with evolutionary multiobjective optimization

The problem of coupling in an efficient way computational fluid dynamics and evolutionary multiobjective optimization codes in order to solve problems of optimal design is discussed. Both the problem of providing an easy to use framework and that of the computational cost are addressed. Moreover, a user interface was designed to allow the execution of different instances, with respect to the parameters of the evolutionary algorithm, of the combined code on several machines from a Grid infrastructure.
A New Method for Comparing the Security of Routing Protocols in Mobile Ad Hoc Networks

Mobile Ad Hoc networks are networks in which users, regardless of geographical locations, can have access to its information and services. These networks are divided into 2 categories: network with structure and network without structure or Ad Hoc. Network structures are composed of fixed gates and wired networks. A mobile host can communicate with the other through a bridge called base station. A mobile host can move geographically while communicating when the host is out of the domain of a base station, it will be connected to another base station and will continue the communication,in this method the base stations are fixed. In contrast to a network structure, all nodes are portable in an Ad Hoc network and communicate dynamically and with a preferred method. All of the nodes in this network act as a router and engage in discovering and route maintenance[1]. The major problem of these networks designing is that these are vulnerable to security attacks. In this paper, the threats aAd Hocnetwork and also security objectives against these threats will be reviewed and some important routing protocols for these kinds of networks, are compared and evaluated. This comparison, unlike previous comparisons, covers wide range of protocols and develops software NS2.
From lattices of phonemes to sentences: a recurrent neural network approach

The author presents preliminary investigations concerning the use of a sequential neutral network for lexical decoding in continuous speech recognition. They explore the architecture introduced by J.L. Elman (1988) for predicting successive elements of a sequence. This recurrent network admits sequential inputs. This model is based on a multilayer architecture and contains special units, called context units, sensitive to the recent activation history of the network. It is suggested that this model be used for lexical decoding in continuous speech recognition. For that purpose, an extension of Elman's model is presented in order to treat erroneous sequential inputs and in order to label patterns. It is suggested that the context units be updated considering their previous values and not only the values of the hidden units. Moreover, output units represent words instead of the prediction on the next phoneme. Preliminary experimental results are given. <<ETX>>
Co-registration-geometrical analysis and verification for SAR interferometry under SRTM data conditions

This paper describes a method that helps to guarantee and verify the accuracy of the co-registration module of an interferometric system. As an example, SRTM mission conditions and data are used. Geometrical predictions are derived for azimuth and range. Finally, the verification approach is shown by comparing theoretical expectations for range co-registration parameters with actual measurements from the data. Potential further applications are Radarsat repeat pass interferometry and may arise with the ENVISAT/Cartwheel configuration.
A Novel Distributed Index Method for Cloud Computing

For the performance and maintenance cost of the existing index methods, this paper presents a distributed multi access entrance B+ tree index method, which has some features that are important in practice, namely (1) achieve efficient parallel interval queries, and (2) and low maintenance cost for index structure. Our scheme relies on four key techniques to achieve efficient parallel interval queries and low maintenance cost for index structure: (1) It gives a routing table to each leaf node of distributed B+ tree, so as to interval search can be completed from any leaf node of any storage node and break through the Bottleneck caused by the root node in the distributed B+ tree; (2) It construction of balanced binary tree in different levels of a node, and choose select the relevant nodes for its routing table; (3) It uses the layer-by-layer transitivity of B+ tree node splitting information to sense node split position so as to only update routing information within subtree of corresponding particle at the time of node splitting; (4) it uses balanced structure of B+ trees to realize only update the single route information of relevant nodes at the time of node splitting, without adjusting the route tables in a large area. Our scheme has been implemented and evaluated, and the performance results are encouraging.
Modeling and Simulation Studies of Dual-Stage Three-Phase Grid-Connected Photovoltaic Systems

Abstract This paper proposes a full detailed mathematical model of a dual-stage, three-phase, gridconnected photovoltaic (PV) system. In this work, the system structure and the modeling techniques of each part of the PV system have been discussed, based on the MATLAB/Simulink software environment, including the PV array, the power-electronic converters (the DC-DC boost converter and the threelevel voltage source converter (3L-VSC)), and the utility interface (the low-pass filter and the coupling transformer). These are followed by introduction of the control schemes (the maximum power point tracking (MPPT) and 3L-VSC control schemes), principles of operation, and modeling steps for gridconnected PV system. A comprehensive set of simulation cases have been conducted to demonstrated the effectiveness of the mathematical model of the PV system. This work can assist power engineers in developing circuit-based simulation models for impact assessment studies, analysis, and identification of potential issues with respect to the grid integration of PV systems.
Determinants Affecting Tax Compliance: A Case of Enterprises In Vietnam

Compliance measurement defined as a basic content of risk management in effective tax management. The objective of this study is to explore the determinants affecting the tax compliance of enterprises in Vietnam. The research result is a science evident for tax managers to improve the tax policy. The researchers surveyed 450 managers related the tax compliance enterprises in Vietnam. They answered 15 questions and 416 samples processed. The primary sources of data collected from November 2017 to November 2018 in Dong Nai, Binh Duong province and Ho Chi Minh City. Simple random sampling technique. The method of the study has three phases following. Phase 1: We applied the expert methodology, phase 2: We tested a reliability scale with Cronbach's Alpha coefficient and Phase 3: We performed CFA and model testing with Structural Equation Modelling (SEM) analysis. Managers’ responses measured through an adapted questionnaire on a 5-point Likert scale. In addition, the findings of the study have three factors affecting the tax compliance of enterprises in Vietnam with significance level 0.01.
Universal Grammar and the acquisition of English L3 syntax

The relationship between linguistic theory and research on language acquisition has changed throughout the years. The main goal of this paper is to frame research on the acquisition of the syntax of a third language (L3) within the Universal Grammar (UG) approach. The paper provides an overview of the role of UG in language acquisition and highlights the findings of recently published research carried out with Basque-Spanish bilinguals who learn English as their third language. The suggestion is that the study of the acquisition of L3 syntax from a UG perspective will be of great interest for the development of a theory of the language faculty.
A new method for image segmentation based on fuzzy knowledge

Within this work a method for knowledge based fuzzy image segmentation is introduced. The basic idea comes from the field of automated medical MRI segmentation where the well-known standard methods have proven insufficient to solve the task. Therefore, a method especially for the problems concerning vagueness in medical imaging has been developed. Beside the improved segmentation procedures, the development has a general impact on the conventional model of image analysis.
Classifying Emotion Using Convolutional Neural Networks

Author(s): Moran, Jonathan L | Abstract: Despite the computer’s historical success as a communication tool, machines themselves have yet to fully master the most basic forms of nonverbal communication that we humans use daily. Gender, ethnicity, age and emotional state is often perceived immediately by most humans engaging in conversation. However, training a classifier algorithm to accomplish this form of behavioral observation is a rather difficult task. In this exploratory review, we will be replicating object recognition and deep learning in a convolutional neural network to ultimately train a model to distinguish the universal human emotions from the FER2013 facial expression dataset (Kaggle, 2013).
A novel LBP based methods for pavement crack detection

Two optimized architectures of Context-based Adaptive Binary Arithmetic Coding (CABAC) are presented in the paper. These are: the software version of CABAC (dedicated to IA-32 and DSP platforms) and the hardware version of CABAC (dedicated to FPGA and ASIC platforms). The paper presents analysis of implementations for both versions of CABAC. The optimized software as well as the hardware version of CABAC was tested in terms of codec throughput.
Neural network identification of poets using letter sequences

Stylistic differences among poets are usually sought in sound and semantics. In human analysis, the criteria for recognizing stylistic differences are manifold and intermingled. This study demonstrates that successful identification of poets based on their work is possible using one criterion: letter sequences. Poets show preferences for certain letter combinations, which are unique to their writing style. Using this criterion in machine computation demonstrates that semantics are not needed to identify poets correctly, and that, as a concession to utter parsimony, one minimal criterion of unique letter sequences is enough to fingerprint an author. A small sample of the work of three Dutch poets was used: Bloem (1887-1966), Slauerhoff (1898-1936), and Lucebert (1924-94). This sample formed the training set for the neural network program to analyse the unique letter patterns for each poet. Next, the program was fed a set of new poems, for which the author was to be identified. In choosing between two poets, the program succeeded in identifying the poet correctly for 80-90% of the new poems. When the choice was between three poets, the score was ∼70% correct. Since raw ASCII files are sufficient as input, and human pre-coding is unnecessary, neural network analysis of letter sequences may turn out to be a powerful tool in categorization and identification problems, such as genre, stylistics, and plagiarism
First and second language visual word recognition : neighbourhood effects in Spanish and English

Current models of visual word recognition assume that the recognition of a stimulus word is affected by orthographically similar words (orthographic neighbourhood). In this thesis I explore the effects of neighbourhood on monolingual and bilingual word recognition. In particular I study the influence of Word Frequency, Neighbourhood Size (N) and Neighbourhood Frequency (NF) in English and Spanish lexical processing. N refers to the number of real words that can be created from a given word by changing one letter at a time while preserving letter position. NF refers to the frequency of the neighbours in relation to the frequency of the target word. There is a great deal of controversy as to whether orthographic neighbours facilitate or inhibit lexical processing and whether neighbourhood effects are consistent across languages. These questions are examined in four experiments carried out within the lexical decision paradigm. Experiment 1 investigates the effects of Word Frequency, N and NF with English stimuli and twenty-four English native speakers. Latency differences are not statistically reliable, but they show a tendency for both N and NF to be facilitative of lexical processing. Experiment 2 examines the same variables with Spanish stimuli and sixty-three Spanish native speakers. Data reveals null effects of N and reliable inhibitory effects of NF, with an interaction of NF with Word Frequency. In Spanish having higher frequency neighbours seems to delay lexical decision times, and this effect appears to be stronger for low frequency words. Experiment 3 explores neighbourhood effects in eighty bilingual speakers ofEnglish and Spanish with bilingual stimuli presented in two language blocks. General results show null effects of N and significant inhibitory effects of NF. Results by target language show reliable facilitative effects ofN in English and highly robust inhibitory effects of NF in Spanish. Experiment 4 further investigates effects of NF in a cross-language