Reviewerly/exHarmony huggingface dataset:
Time-Bounded Best-First Search

Time-Bounded A* (TBA*) is a single-agent deterministic search algorithm that expands states of a graph in the same order as A* does, but that unlike A* interleaves search and action execution. Although the idea underlying TBA* can be generalized to other single-agent deterministic search algorithms, little is known about the impact on performance that would result from using algorithms other than A*. In this paper we propose Time-Bounded Best-First Search (TB-BFS) a generalization of the time-bounded approach to any best-first search algorithm. Furthermore, we propose restarting strategies that allow TB-BFS to solve search problems in dynamic environments. In static environments, we prove that the resulting framework allows agents to always find a solution if such a solution exists, and prove cost bounds for the solutions returned by Time-Bounded Weighted A* (TB-WA*). We evaluate the performance of TB-WA* and Time-Bounded Greedy Best-First Search (TB-GBFS). We show that in pathfinding applications in static domains, TB-WA* and TB-GBFS are not only faster than TBA* but also find significantly better solutions in terms of cost. In the context of videogame pathfinding, TB-WA* and TB-GBFS perform fewer undesired movements than TBA*. Restarting TB-WA* was also evaluated in dynamic pathfinding random maps, where we also observed improved performance compared to restarting TBA*. Our experimental results seem consistent with theoretical bounds.
On Different Strategies for Eliminating Redundant Actions from Plans

Satisficing planning engines are often able to generate plans in a reasonable time, however, plans are often far from optimal. Such plans often contain a high number of redundant actions, that are actions, which can be removed without affecting the validity of the plans. Existing approaches for determining and eliminating redundant actions work in polynomial time, however, do not guarantee eliminating the "best" set of redundant actions, since such a problem is NP-complete. We introduce an approach which encodes the problem of determining the "best" set of redundant actions (i.e. having the maximum total-cost) as a weighted MaxSAT problem. Moreover, we adapt the existing polynomial technique which greedily tries to eliminate an action and its dependants from the plan in order to eliminate more expensive redundant actions. The proposed approaches are empirically compared to existing approaches on plans generated by state-of-the-art planning engines on standard planning benchmarks.
Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems

In this paper, we extend the Compact Genetic Algorithm (CGA) for real-valued optimization problems by dividing the total search process into three stages. In the first stage, an initial vector of probabilities is generated. The initial vector contains the probabilities of bits having 1 depending on the bit locations as defined in the IEEE-754 standard. In the second stage, a CGA search is applied on the objective function using the same encoding scheme. In the last stage, a local search is applied using the result obtained by the previous stage as the starting point. A simulation study is performed on a set of well-known test functions to measure the performance differences. Simulation results show that the improvement in search capabilities is significant for many test functions in many dimensions and different levels of difficulty.
Extended Framework for Target Oriented Network Intelligence Collection

The Target Oriented Network Intelligence Collection (TONIC) problem is the problem of finding profiles in a social network that contain publicly available information about a given target profile via automated crawling. Such profiles are called leads. Leads can be found by crawling the network using the profiles' friend lists (immediate neighborhood) in order to decide which profile will be crawled next. Assuming that leads tend to cluster together, prior work limited the search for new leads only to immediate neighbors of the leads previously found. In this paper we relax this limitation, and extend the scope of the search to a wider neighborhood, including the possibility of crawling to non-leads, i.e., profiles that have no publicly available information about the target. We propose a set of heuristics that guide this search. Experimental results show that with the new setting more leads can be found and leads are found faster. In addition, we perform a cost benefit analysis of the search, weighing the reward of finding leads with the costs of the search.
The Data Dilemma

The Data Dilemma There's a famous allegory about a map of the world that grows in detail until every point in reality has its counterpoint on paper; the twist being that such a map is at once ideally accurate and entirely useless, since it's the same size as the thing it's meant to represent 1 .
The Contribution of Education to Economic Development

Education is a comprehensive term for academic studies and developing talents and skills. It's an essential factor in Human capital development. Human capital investment is a fundamental issue for countries to sustainable economic development achievement; furthermore, it sang the community members to grasp themselves and the surrounding circumstances and the impact of the global variants. Education improves the quality of living, benefits society; productivity and innovation; subsequently promotes entrepreneurship. Education helps the poor and improves their food intake by increasing families' incomes and spending on health and food, to raise their standard of living and make them better healthier choices.
Bounded Suboptimal Search in Linear Space: New Results

Bounded suboptimal search algorithms are usually faster than optimal ones, but they can still run out of memory on large problems. This paper makes three contributions. First, we show how solution length estimates, used by the current state-of-the-art linear-space bounded suboptimal search algorithm Iterative Deepening EES, can be used to improve unbounded-space suboptimal search. Second, we convert one of these improved algorithms into a linear-space variant called Iterative Deepening A* epsilon, resulting in a new state of the art in linear-space bounded suboptimal search. Third, we show how Recursive Best-First Search can be used to create additional linear-space variants that have more stable performance. Taken together, these results significantly expand our armamentarium of bounded suboptimal search algorithms.
TINKLARAŠČIO MEDIJOS ESMINIAI BRUOŽAI IR PANAUDOJIMO GALIMYBĖS

This article analyse fundamental blog media features and overviews the blog use possibilities. The research is being conducted using theoretical induction, deduction and analogy research methods. Focuses in the short social media history, identified areas of research, analyse blog’s content characteristics, blog media definitions and classifications are compared.
Modeling Self-Adapting and Multicultural Non-Player Characters in Artificial Worlds

Modeling the behavior of Non-Player Characters (NPCs) so that they can act properly in an artificial world is a difficult, yet essential task in the development of modern computer games, often requiring never- ending adjustments of handwritten rules in a way to enable NPCs to perform well in the game world. This pa- per introduces an evolutionary method relying on high-level rule-based behaviors, genetic algorithms and speciation. By interconnecting these three areas, we pro- pose a technique that has the potential to provide multicultural NPCs with the ability to adapt themselves to an artificial worl
Modeling the Evolution of Knowledge in Learning Systems

How do reasoning systems that learn evolve over time? What are the properties of different learning strategies? Characterizing the evolution of these systems is important for understanding their limitations and gaining insights into the interplay between learning and reasoning. We describe an inverse ablation model for studying how large knowledge-based systems evolve: Create a small knowledge base by ablating a large KB, and simulate learning by incrementally re-adding facts, using different strategies to simulate types of learners. For each iteration, reasoning properties (including number of questions answered and run time) are collected, to explore how learning strategies and reasoning interact. We describe several experiments with the inverse ablation model, examining how two different learning strategies perform. Our results suggest that different concepts show different rates of growth, and that the density and distribution of facts that can be learned are important parameters for modulating the rate of learning.
Engineering Design: The Information Component

The curriculum of the Bucknell University Chemical Engineering Department includes a required senior year capstone course titled Process Engineering, with an emphasis on process design.For the past ten years library research has been a significant component of the coursework, and students working in teams meet with the librarian throughout the semester to explore a wide variety of information resources required for their project.The assignment has been the same from 1989 to 1999.Teams of students are responsible for designing a safe, efficient, and profitable process for the dehydrogenation of ethylbenzene to styrene monomer.A series of written reports on their chosen process design is a significant course outcome.While the assignment and the specific chemical technology have not changed radically in the past decade, the process of research and discovery has evolved considerably.This paper describes the solutions offered in 1989 to meet the information needs of the chemical engineering students at Bucknell University, and the evolution in research brought about by online databases, electronic journals, and the Internet, making the process of discovery a completely different experience in 1999.
Multi Robot Systems: The EagleKnights/RoboBulls Small- Size League RoboCup Architecture

In this paper we present the system architecture of the Eagle Knights/RoboBulls Small Size League RoboCup Team.In this league two teams composed of five autonomous robots each compete against each other in a medium size field.This league is one of the fastest and most thrilling in RoboCup permitting teams to develop complex coordination strategies.We explain the three main components of the architecture: Vision System, AI System and Robots.
Clustering Documents Along Multiple Dimensions

Traditional clustering algorithms are designed to search for a single clustering solution despite the fact that multiple alternative solutions might exist for a particular dataset. For example, a set of news articles might be clustered by topic or by the author's gender or age. Similarly, book reviews might be clustered by sentiment or comprehensiveness. In this paper, we address the problem of identifying alternative clustering solutions by developing a Probabilistic Multi-Clustering (PMC) model that discovers multiple, maximally different clusterings of a data sample. Empirical results on six datasets representative of real-world applications show that our PMC model exhibits superior performance to comparable multi-clustering algorithms.
Performance Appraisal Decision and Cognitive Processing Model: A Case of Novice and Expert Raters in the Educational Organization

This paper explores rater (novice and expert) understanding of the decision-making process in the performance appraisal system (PAS) by looking at the cognitive processing model (CPM). Interviews were conducted using a semi-structured cognitive mapping protocol involving 22 Malaysian public school administrators and were analysed using key word analysis. The decision explorer software was used to map the thinking activities involved in the rater decision-making process. The findings revealed that raters recognised the CPM steps in their performance appraisal practice. The study also identified the differences between individual novice and expert raters’ CPM in terms of concept and complexity, and also an attempt was made to compare the CPM practised by the raters with that suggested in the research framework. Every cognitive map by the raters has its own uniqueness and represents raters capability to process relevant information involved during the performance appraisal decision-making process. This paper contributes to the body of knowledge by offering a new perspective to understanding performance rating by looking at the steps involved in CPM. The research also offered some insights to policy makers, practitioners, researchers, and scholars in order to undertake further research and analysis using the steps in the CPM in a new issue, context, and environment.
Narrative Intelligence Without (Domain) Boundaries

Narrative Intelligence (NI) can help computational systems interact with users, such as through story generation, interactive narratives, and believable virtual characters. However, existing NI techniques generally require manually coded domain knowledge, restricting their scalability. An approach that intelligently, automatically and economically acquires script-like knowledge in any domain with strategic crowdsourcing will ease this bottleneck and broaden the application territory of narrative intelligence. This doctoral consortium paper defines the research problem, describes its significance, proposes a feasible research plan towards a Ph.D. dissertation, and reports on its current progress.
A Study on Financial Time Series Forecasting and Symbolic Regression by means of a Hybrid Probabilistic Model-Building Cartesian Genetic Programming Methodology

This work proposes a hybrid algorithm called Probabilistic Incremental Cartesian Genetic Pro- gramming (PI-CGP), which integrates an Estimation of Distribution Algorithm (EDA) with Carte- sian Genetic Programming (CGP). PI-CGP uses a fixed-length problem representation and the algorithm constructs a probabilistic model of promising solutions. PI-CGP was evaluated on sym- bolic regression problems and next trading day stock price forecasting. On the symbolic regression problems PI-CGP did not outperform other approaches. The reason could be premature convergence and being trapped at a local minimum. However, PI-CGP was competitive at stock market forecasting. It was comparable to a fusion model employing a Hidden Markov Model (HMM). HMMs are extensively used for time-series forecasting. This result is promising considering the volatile nature of the stock market and that PI-CGP was not customized toward forecasting.
A Formal Game for Eliciting Story Structure from Authors

We address the problem of determining the structure of a set of plot points for an interactive narrative. To do so, we define a formal two-player game where a computer can play with an author to learn the structural representation of the story. This technique will allow for authors unfamiliar, or uncomfortable, with mathematical structures to create the inputs interactive narrative algorithms require. We include the underlying mathematical theory as a foundation of our approach, and characterize it's effectiveness through a series of simulation experiments. Results indicate there is promise in using formal games to aid in authoring interactive narrative structures.
Adaptive Mobile Web Pages Using HTML5 and CSS for Engineering Faculty

Many engineering faculty members have their class materials like lecture slides and assignments posted on the Internet, so students can easily access them anytime.Nowadays students also like to access the online class materials anywhere via their mobile devices such as smartphones or tablet computers.However, traditional Web pages are distorted or become awkward to use when they are displayed on devices.Additionally, mobile features of handheld devices such as geolocation information, which could be useful for engineering education, are usually ignored.The introduction of HTML5 and the newest CSS have greatly solved this problem.This paper introduces the mobile features of HTML5 and CSS to engineering faculty for building adaptive mobile class Web pages.
PaTux: An Authoring Tool for Level Design through Pattern Customisation Using Non-Negative Matrix Factorization

We present a demonstration of PaTux, an authoring tool for designing levels in SuperTux game through combining patterns. PaTux allows game designers to specify the design of their levels using patterns extracted from training level samples. The Non-negative Matrix Factorisation (NMF) method is utilised to approximate pattern and weight matrices from the training data. The patterns are visualised for designers to choose from and the changes made on the level structure are visualised in realtime. The designer can also specify the weight of each pattern permitting exploration of a wider variety. The data used to train the model can also be specified by the designer resulting in learning a new set of patterns. The system also suggests variations for a given design. When the designer is satisfied with the design, the system allows loading the resultant level in the game to be played.
Dealing with Failures of Assumptions in Analyses of Medical Care Quality Indicators with Large Databases Using Clustering

The application of linear mixed models or generalized linear mixed models to large databases in which the level 2 units (hospitals) have a wide variety of characteristics is a problem frequently encountered in studies of medical quality.Accurate estimation of model parameters and standard errors requires accounting for the grouping of outcomes within hospitals.Including the hospitals as random effect in the model is a common method of doing so.However in a large, diverse population, the required assumptions are not satisfied, which can lead to inconsistent and biased parameter estimates.One solution is to use cluster analysis with clustering variables distinct from the model covariates to group the hospitals into smaller, more homogeneous groups.The analysis can then be carried out within these groups.We illustrate this analysis using an example of a study of hemoglobin A1c control among diabetic patients in a national database of United States Department of Veterans' Affairs (VA) hospitals.
Around the Water Cooler: Shared Discussion Topics and Contact Closeness in Social Search

Search engines are now augmenting search results with social annotations, i.e., endorsements from users’ social network contacts. However, there is currently a dearth of published research on the effects of these annotations on user choice. This work investigates two research questions associated with annotations: 1) do some contacts affect user choice more than others, and 2) are annotations relevant across various information needs. We conduct a controlled experiment with 355 participants, using hypothetical searches and annotations, and elicit users’ choices. We find that domain contacts are preferred to close contacts, and this preference persists across a variety of information needs. Further, these contacts need not be experts and might be identified easily from conversation data.
Developing pseudo random number generator based on neural networks and neurofuzzy systems

Pseudo random number generators (PRNGs) are one of the most important components in security and cryptography applications. We propose an application of Hopfield Neural Networks (HNN) as pseudo random number generator. This research is done based on a unique property of HNN, i.e., its unpredictable behavior under certain conditions. Also, we propose an application of Fuzzy Hopfield Neural Networks (FHNN) as pseudo random number generator. We compare the main features of ideal random number generators with our proposed PRNGs. We use a battery of statistical tests developed by National Institute of Standards and Technology (NIST) to measure the performance of proposed HNN and FHNN. We also measure the performance of other standard PRNGs and compare the results with HNN and FHNN PRNG. We have shown that our proposed HNN and FHNN have good performance comparing to other PRNGs accordingly.
Combining Probabilistic Planning and Logic Programming on Mobile Robots

Key challenges to widespread deployment of mobile robots to interact with humans in real-world domains include the ability to: (a) robustly represent and revise domain knowledge; (b) autonomously adapt sensing and processing to the task at hand; and (c) learn from unreliable high-level human feedback. Partially observable Markov decision processes (POMDPs) have been used to plan sensing and navigation in different application domains. It is however a challenge to include common sense knowledge obtained from sensory or human inputs in POMDPs. In addition, information extracted from sensory and human inputs may have varying levels of relevance to current and future tasks. On the other hand, although a non-monotonic logic programming paradigm such as Answer Set Programming (ASP) is wellsuited for common sense reasoning, it is unable to model the uncertainty in real-world sensing and navigation (Gelfond 2008). This paper presents a hybrid framework that integrates ASP, hierarchical POMDPs (Zhang and Sridharan 2012) and psychophysics principles to address the challenges stated above. Experimental results in simulation and on mobile robots deployed in indoor domains show that the framework results in reliable and efficient operation.