Reviewerly/exHarmony huggingface dataset:
Evaluation of Attribute Selection Measures in Decision Tree induction

Many attribute selection measures have been proposed for decision tree induction, but little was known regarding their experimental comparative evaluation. This paper aimed at two following objectives. One was the introduction of a new attribute selection measure (R-measure) for decision tree induction. The second was an experimental evaluation of the performance of inductive systems using R-measure and eight different attribute selection measures used by machine learning community: gain-ratio, gini-index, gini’-index, Relief, J-measure, dN distance, relevance, χ 2.
PENGUKURAN CAPABILITY LEVEL TATA KELOLA DAN MANAJEMEN TI MENGGUNAKAN COBIT 5.0 PADA PT WELLCOMM RITELINDO PRATAMA

In this study, researchers wanted to find out the level of information technology in the company through measuring the capability level of governance and information technology management at PT Wellcomm Ritelindo Pratama using the COBIT 5.0 framework. There are 3 research methods used, that is: observation, interview, and checklist. The first is to hold discussions about the goals of PT Wellcomm Ritelindo Pratama based on enterprise goals in COBIT 5.0. The second is mapping the company goals / enterprise goals to the IT goals of the company / it goals. Third is to enter into the process in COBIT 5.0. From the calculation results obtained from the process questionnaire is to determine the capability level of the process (target maturity) and whether the process can continue until the last level (5) or must stop at the initial level (1). The final step is to provide recommendations based on the final capability level and target capability level (next level). The results of measuring capability capability management and IT management using COBIT 5.0 PT Wellcomm Ritelindo Pratama, found several weaknesses that must be a concern of management in the future. The recommendations given are based on two targets: the target is carried out fully, so that the company can carry out each activity and complete it so that it can be said that the activity is carried out fully, and the target to be able to move up to the next level.
Deciding What to Display: Maximizing the Information Value of Social Media

In information-rich environments, the competition for users' attention leads to a flood of content from which people often find hard to sort out the most relevant and useful pieces. Using Twitter as a case study, we applied an attention economy solution to generate the most informative tweets for its users. By considering the novelty and popularity of tweets as objective measures of their relevance and utility, we used the Huberman-Wu algorithm to automatically select the ones that will receive the most attention in the next time interval. Their predicted popularity was confirmed by using Twitter data collected for a period of 2 months.
Preliminary Material

Three independent phenomena have appeared over the last fifteen years that make it possible to think about linguistics in a different way. The first is the appearance of the World Wide Web; the second is the development of robust and rapid, though shallow, linguistic analyzers, and the third is the availability of cheap memory. In this chapter, we show how these three phenomena come together in the creation of a new linguistic resource: the Very Large Lexicon.
The effect of using YouTube as a didactic support on microeconomy's grades

Recent research has shown that using YouTube videos as teaching material improves student’s grades in online master programs. This article shows that the use of videos for undergraduate students in face-to-face programs improves by 3.54% the average grade of students treated. A procedure based on Randomized Controlled Experiments (RCT) was followed, where the treatment is controlled by different observable characteristics about the high school education of the students, their scores university admission tests, their access to internet connection and their study habits. These results confirm the importance of the use of new technologies in face-to-face learning at undergraduate programs.
Sparse Probabilistic Relational Projection

Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretability because each principal component is a linear combination of all the original variables. In this paper, we propose a novel model, called sparse probabilistic relational projection (SPRP), to learn a sparse projection matrix for relational dimensionality reduction. The sparsity in SPRP is achieved by imposing on the projection matrix a sparsity-inducing prior such as the Laplace prior or Jeffreys prior. We propose an expectation-maximization (EM) algorithm to learn the parameters of SPRP. Compared with PRPCA, the sparsity in SPRP not only makes the results more interpretable but also makes the projection operation much more efficient without compromising its accuracy. All these are verified by experiments conducted on several real applications.
Abstract: Block A* and Any-Angle Path-Planning

We present three new ideas for grid-based path-planning algorithms that improve the search speed and quality of the paths found. First, we introduce a new type of database, the Local Distance Database (LDDB), that contains distances between boundary points of a local neighborhood. Second, an LDDB-based algorithm is introduced, called Block A*, that calculates the optimal path between start and goal locations given the local distances stored in the LDDB. Third, our experimental results for any-angle path planning in a wide varietyof test domains, including real game maps, show that Block A* is faster than both A* and the previously best grid-based any-angle search algorithm, Theta*.
The challenges and opportunities for mega-infrastructure projects and archaeology: Response to the Respondents

When I was asked to provide a lead paper for this forum the title seemed so wide-ranging that it took a little while to decide how to approach it. After due consideration I decided to focus on what I feel is the key to better integration of archaeology into large scale projects. Simply put, the theme is perhaps analogous to earlier developments in archaeology. Archaeologists have long adopted technological solutions from other disciplines to find, record, and analyse deposits, and they have sought out social and anthropological theories to interpret their results. I am stressing here that in order to integrate archaeology successfully into complex mega-projects and to allow the discipline to grow in this environment, we should adopt and adapt again.
A dynamic study with side channel against An Identification Based Encryption

Recently, the side channel keeps the attention of researchers in theory of pairing, since, several studies have been done in this subject and all them have the aim in order to attack the cryptosystems of Identification Based Encryption (IBE) which are integrate into smart cards (more than 80% of those cryptosystems are based on a pairing). The great success and the remarkable development of the cryptography IBE in the recent years and the direct connection of this success to the ability of resistance against any kind of attack, especially the DPA and DFA attacks, leave us to browse saying all the studies of the DPA and DFA attacks applied to a pairing and we have observed that they have no great effect to attack the cryptosystems of IBE. That’s what we will see in this paper. In this work we will illuminate the effect of the DPA attack on a cryptosystems of IBE and we would see on what level we can arrive. Thus in the case where this attack can influence on those cryptosystems, we can present an appropriate counter measures to resist such attack. In the other part we will also propose a convenient counter-measure to defend the attack DFA when the embedding degree is even
Extraction of Topical Consumer Products from Weblogs

This paper proposes a new algorithm of associated topic extraction, which detects related topics in a collection of blog entries commenting on a specified topic. The main feature of the algorithm is to evaluate how important a topic is to the collection, according to the popularity of blog entries through Trackbacks and comments. Another feature is to utilize product ontology for excluding unrelated topics. Evaluation results show that the proposed algorithm can capture users' impressions of associated topics more accurately than TF-IDF.
Computer Support for Discussions in Spatial Planning

1. Introduction Spatial planning deals with the problem of distributing the limited resource "space" among different uses and users. It can be highly challenging to find a balanced land-use pattern, for example in urban agglomerations. Different interest groups such as residents, industry, and ecologists will claim different desirable land-uses for a given area. Spatial planning is also about locating unwanted land-use such as waste facilities. In this case, interest groups (e.g. city councils, neighbourhood organizations) and individuals will fight nearby locations. This situation is known as the NIMBY problem: “Not In My BackYard!” In democratic societies, decisions such as those in spatial planning are made by political representatives in cooperation with public administration and residents. The final decision will usually be based on a number of consecutive prior decisions, or choices, which are made by different groups of stakeholders. At any of these decision levels, there are two important methods to reach a conclusion: consensus finding, or voting. Both will be preceded by more or less intensive discussions and argumentation. The ultimate goal of discussions is to achieve sustainable development by integrating the objectives of diverse stakeholders. Thus, we argue that discussions are a crucial element of spatial planning procedures and are to be integrated with planning and decision support techniques. Discussions will have diverse formats in different planning projects. For example, the number of participants may vary from only two to hundreds and more; participants may get together or stay separated in space and/or time; discussion may be un-moderated, or moderated and structured. Nevertheless, discussion contributions (statements, messages, arguments, articles) in spatial planning will commonly contain a spatial reference. This does allow to link discussion support to spatially enabled decision support techniques as argued in this chapter. In section 2, we will review general theories on argumentation and introduce major concepts of computer-supported cooperative work. Next, geographically referenced discourse will be analysed in more detail leading to the argumentation map model (section 3). Section 4 develops use cases for GIS-based discussion support, and section 5 presents some existing applications. Finally, we will speculate about future developments in computer support for discussions in spatial planning (section 6).
An Optimized Web Feed Aggregation Approach for Generic Feed Types

Web feeds are a popular way to access updates for contentin the World Wide Web. Unfortunately, the technology be-hind web feeds is based on polling. Thus, clients ask the feedserver regularly for updates. There are two concurrent prob-lems with this approach. First, many times a client asks forupdates, there is no new item and second, if the client’s up-date interval is too large it might be notiﬁed too late or evenmiss items. In this work we present adaptive feed polling algorithms. Thealgorithms learn from the previous behaviors of feeds andpredict their future behaviors. To evaluate these algorithmswe created a real set of over 180,000 diversiﬁed feeds andcollected a dataset of their updates for a time of three weeks.We tested our adaptive algorithms on this set and show thatadaptive feed polling reduces trafﬁc signiﬁcantly and pro-vides near-real-time updates.
Sequence Labeling with Non-Negative Weighted Higher Order Features

In sequence labeling, using higher order features leads to high inference complexity. A lot of studies have been conducted to address this problem. In this paper, we propose a new exact decoding algorithm under the assumption that weights of all higher order features are non-negative. In the worst case, the time complexity of our algorithm is quadratic on the number of higher order features. Comparing with existing algorithms, our method is more efficient and easier to implement. We evaluate our method on two sequence labeling tasks: Optical Character Recognition and Chinese part-of-speech tagging. Our experimental results demonstrate that adding higher order features significantly improves the performance while requiring only 30% additional inference time.
Planning Under Time Pressure

Heuristic search is a technique used pervasively in the fieldsof artificial intelligence, automated planning and operations research to solve a wide range of problems from planning military deployments to planning tasks for a robot that must clean a messy kitchen. An automated agent can use heuristic search to construct a plan that, when executed, will achieve a desired task. The search algorithm explores different sequences of actions that the agent can execute, looking for a sequence that will lead it to a desired goal state. In many situations, an agent is given a task that it would like to solve as quickly as possible. The agent must allocate its time between searching for the actions that will achieve the task and actually executing them. We call this problem planning under time pressure.
Evolving Solvers for FreeCell and the Sliding-Tile Puzzle

We use genetic algorithms to evolve highly successful solvers for two puzzles: FreeCell and the Sliding-Tile Puzzle.
Book reviews [7 books reviewed]

transmission and distribution systems measured.By measuring and examining thresholds and patterns in a magnetic conditions that relate to some desired predictive diagnostic or measured quantity.This book focuses on the most re-
Identifying Microblogs for Targeted Contextual Advertising

Micro-blogging sites such as Facebook, Twitter, Google+ present a nice opportunity for targeting advertisements that are contextually related to the microblog content. By virtue of the sparse and noisy text makes identifying the microblogs suitable for advertising a very hard problem. In this work, we approach the problem of identifying the microblogs that could be targeted for advertisements as a two-step classification approach. In the first pass, microblogs suitable for advertising are identified. Next, in the second pass, we build a model to find the sentiment of the advertisable microblog. The systems use features derived from the Part-of-speech tags, the tweet content and uses external resources such as query logs and n-gram dictionaries from previously labeled data.This work aims at providing a thorough insight into the problem and analyzing various features to assess which features contribute the most towards identifying the tweets that can be targeted for advertisements.
Toward Narrative Schema-Based Goal Recognition Models for Interactive Narrative Environments

Computational models for goal recognition hold great promise for enhancing the capabilities of drama managers and director agents for interactive narratives. The problem of goal recognition, and its more general form, plan recognition, have been the subjects of extensive investigation in the AI community. However, relatively little effort has been undertaken to examine goal recognition in interactive narrative. In this paper, we propose a research agenda to improve the accuracy of goal recognition models for interactive narratives using explicit representations of narrative structure inspired by the natural language processing community. We describe a particular category of narrative representations, narrative schemas, that we anticipate will effectively capture patterns of player behavior in interactive narratives and improve the accuracy of goal recognition models.
SEER: A Graphical Tool for Multidimensional and Categorical Data

This paper introduces a visualization technique, SEER, developed for policy makers and researchers to graphically analyze and explore massive amounts of categorical data collected in longitudinal surveys.This technique (a) produces panels of graphs for multiple group analysis, where the groups do not have to be mutually exclusive, (b) profiles change patterns observed in longitudinal data, and (c) clusters data into groups to enable policy makers or researchers to observe the factors associated with the changing patterns.This paper also includes the hash function, of the SEER method, expressed in matrix notation for it to be implemented across computer packages.The SEER technique is illustrated by using a national survey, the Survey of Doctorate Recipients (SDR), administered by the National Science Foundation (NSF).Occupational changes and career paths for a panel sample of 14,901 doctorate recipients are profiled and discussed.Results indicated that doctorate recipients in some science and engineering fields are roughly two times more likely to work in an occupation when it is the discipline in which they received their doctorates.
An Application of Bayesian Model Averaging Approach to Traffic Accidents Data Over Hierarchical Log-Linear Models

In this article, a Bayesian model averaging approach for hierarchical log-linear models is considered.Posterior model probabilities are approximately calculated for hierarchical log-linear models.Dimension of interested model space is reduced by using Occam's window and Occam's razor approaches.2002 road traffic accident data of Turkey is analyzed by using the considered approach.
Psychometric Data Analysis: A Size/fit Trade-off Evaluation Procedure for Knowledge Structures

A crucial problem in knowledge space theory, a modern psychological test theory, is the derivation of a realistic knowledge structure representing the organization of knowledge in an information domain and examinee population under reference.Often, one is left with the problem of selecting among candidate competing knowledge structures.This article proposes a measure for the selection among competing knowledge structures.It is derived within an operational framework (prediction paradigm), and is partly based on the unitary method of proportional reduction in predictive error as advocated by the authors Guttman, Goodman, and Kruskal.In particular, this measure is designed to trade off the (descriptive) fit and size of a knowledge structure, which is of high interest in knowledge space theory.The proposed approach is compared with the Correlational Agreement Coefficient, which has been recently discussed for the selection among competing surmise relations.Their performances as selection measures are compared in a simulation study using the fundamental basic local independence model in knowledge space theory.
E-LEARNING/M-LEARNING – THE NEW TREND IN FOREIGN LANGUAGE TEACHING

The article deals with the importance of e-learning/m-learning in acquiring a foreign language. As the amount of information that must be accumulated in school/university has increased significantly in recent years, the pupil/student nowadays is moving towards using computers as a means of information in the learning process. The article also presents the advantages and disadvantages of e-learning and some examples of online platforms that offer English courses. A new concept is introduced, namely mobile learning. M-learning is a new method, a relatively new concept in the learning process. M-learning means that learning does not only happen at a desk, nowadays students should have constant access to information no matter the place they are. The conclusion is that e-learning/m-learning cannot completely replace the classical methods of learning, but can facilitate and improve the learning process.
Embedded Patient Monitoring System

&lt;p&gt;ICU remains for Intensive Care Unit, a place in the recuperating office where wiped out patients are checked eagerly. Commonly, the patient-staff extent is low and the LIFE-SAVING EQUIPMENT used is outstandingly bleeding edge Generally ICU is a healing facility for course of action of genuine nursing and remedial consideration of essentially wiped out patients, depicted by high bore and measure of unending nursing and restorative supervision and by use of cutting edge checking and resuscitative equipment The patients in the ICU require a predictable seeing of their Temperature and pulse circulatory strain. This undertaking is a working model, which wires sensors to evaluate imperative parameters specifically the Temperature, Respiratory temperature and Heart Beat. The sensors are interfaced to PC, with the objective that the condition of a patient can be explored by masters in any bit of the center wherever they are. At whatever point there is a variety from the standard felt by the patient, the particular patient will give an alert movement, by which the pro can race to the patient. Despite when the patient is in a careless condition, each one of the parameters will be identified and pro will be admonished, thusly it diminishes master's remaining task at hand and besides gives more correct results .Our endeavor is a working model which wires sensors to measure each one of these parameters like body temperature, Respiratory Temp and Heart Beat rate and trade it to the PC, with the objective that the patient condition can be examined to by authorities in any bit of the recuperating focus wherever they are. In this way it decreases experts work stack and besides gives more exact results, wherever there is a variety from the standard felt by the patient, we have in like manner combined saline watching system which gives an alert when the saline container going to cleanse.&lt;/p&gt;
Learning Micro-Management Skills in RTS Games by Imitating Experts

We investigate the problem of learning the control of small groups of units in combat situations in Real Time Strategy (RTS) games. AI systems may acquire such skills by observing and learning from expert players, or other AI systems performing those tasks. However, access to training data may be limited, and representations based on metric information -- position, velocity, orientation etc. -- may be brittle, difficult for learning mechanisms to work with, and generalise poorly to new situations. In this work we apply \textit{qualitative spatial relations} to compress such continuous, metric state-spaces into symbolic states, and show that this makes the learning problem easier, and allows for more general models of behaviour. Models learnt from this representation are used to control situated agents, and imitate the observed behaviour of both synthetic (pre-programmed) agents, as well as the behaviour of human-controlled agents on a number of canonical micro-management tasks. We show how a Monte-Carlo method can be used to decompress qualitative data back in to quantitative data for practical use in our control system. We present our work applied to the popular RTS game Starcraft.
Large-Scale Mapping and Navigation in VirtualWorlds: Thesis Summary

Virtual worlds present a challenge for intelligent mobile agents. They are required to generate maps of very large scale, dynamic and unstructured environments in a short amount of time. We investigate how to represent maps of ever growing virtual environments, how the agent can build, update and use these maps to navigate between points in the environment. We look at trails, the movement of other people and agents in the environment as a new information source. We can use trails to improve the generation of probabilistic roadmaps in these environments and enable the agent to segment space intelligently. Our future plans are to extend this to look at dynamic environments, where the agent will have to recognise change and update the map and how this will affect the map representation.