Reviewerly/exHarmony huggingface dataset:
An Ethnomethodologically-Informed Approach to Interface Design for Social Interactions around Video Online

With the emergence of video on community media sites such as YouTube or TED, there is a need to understand interactions by community participants around video in order to maximise the potential of these systems to support communities and create meaningful interactions online. In this paper, we illustrate how ethnomethodologically-informed studies of the social interactions among a small group of co-located participants around video may be used to inform the design of an online video interface which may support interactions by a large distributed group of participants. Our point of view is grounded in the idea that the interactional accomplishments of a co-located group remain relevant at a distributed level, thereby allowing an ethnomethodologically-informed approach to arrive at effective implications for design of online systems. A strength of this approach is the potential to create implications which are properly grounded in a set of observations of the precise ways in which people interact to accomplish social interaction around video. For the purpose of illustration, we perform an analysis of a fragment of video data collected from a quasi-naturalistic experiment of a co-located group collaboratively annotating a video, from which we propose a number of design implications for a video annotation interface.
Learning Heuristic Functions Faster by Using Predicted Solution Costs

Jabbari Arfaee, Zilles, and Holte presented the bootstrap learning system, a system that learns strong heuristic functions for state-space problems. They showed that IDA* with a bootstrap heuristic is able to quickly find near-optimal solutions in several problem domains. However, the process the bootstrap method uses to learn heuristic functions is time-consuming: it is on the order of days. In this paper we present a learning system that uses an approximation method instead of an exact one to generate the training set required to learn heuristics. We showed recently that solution costs can often be quickly and accurately predicted without having to actually find a solution. In this paper we apply this idea to speedup the process of learning heuristics. In contrast with other learning approaches that use search algorithms to solve problem instances to generate the training set, our system uses a solution cost predictor. We reduce the time required to learn strong heuristics from days to minutes on the domains tested.
Real-Time Optimization-Based Planning in Dynamic Environments Using GPUs

We present a novel algorithm to compute collision-free trajectories in dynamic environments. Our approach is general and makes no assumption about the obstacles or their motion. We use a replanning framework that interleaves optimization-based planning with execution. Furthermore, we describe a parallel formulation that exploits high number of cores on commodity graphics processors (GPUs) to compute a high-quality path in a given time interval. Overall, we show that search in configuration spaces can be significantly accelerated by using GPU parallelism.
Formal expressive indiscernibility underlying a prosodic deformation model

We are here concerned by the setting up of a model and a formalism for expressive speech synthesis under the paradigm of a corpus-based approach.Our objective is to apply prosodic expressive forms, acquired from natural humanreading recordings, on a new textual matter.We outline a general model for speech expressiveness.Then we deal with some formal aspects of expressive representation.We point out the core transformational aspects and the indiscernibility criteria allowing comparisons be-tween forms.We finish by some interpretational issues of such an approach.
Optimum design of 3R robot manipulador by using Improved Differential Evolution implemented in parallel computation

&#x0D; &#x0D; &#x0D; In recent decades the great interest in Evolutionary Algorithms (EAs) has boosted their development leading to a significant improvement in their efficiency and applicability. Thus, EAs have been applied to solve optimization problems in different areas of knowledge. A promising optimization method known as Differential Evolution (DE), which belongs to the class of AEs, has attracted the attention of researchers. The DE algorithm is simple, robust and efficient. However, by testing with classical optimization problems noticed that sometimes the results obtained with DE are not as satisfactory as expected or that in many cases the algorithm ends the search for the optimal solution prematurely. Recently, with the advancement and greater availability of computer technology, the scientific community has been thinking about the implementation of optimization algorithms in parallel in order to reduce the processing time. The main objective of this paper is to present an improvement of the Differential Evolution optimization method, proposing modifications to the basic algorithm by using shuffled complex and making it able to work with parallel computing. The proposed methodology is applied to the optimal design of an orthogonal 3R robot manipulator that takes into account the characteristics of its workspace. For this purpose, a multi-objective optimization problem is formulated to obtain the optimal geometric parameters for the robot. The maximum workspace volume, the maximum system stiffness and the optimum dexterity are considered as the multi-objective functions. The results show that the procedure represents a promising alternative for the type of problem presented above.&#x0D; &#x0D; &#x0D;
SISTEM PERINGATAN DINI KETAHANAN PANGAN DAERAH: PENATALAKSANAAN DAN PELIBATAN SELURUH PEMANGKU KEPENTINGAN

Early Warning System (EWS) is a solution of food security of the most important in the management of the policy (Governance) food security. This study departs from best practices (best practices) in the regional autonomy that has been applied in the area surrounding the Surakarta and management of food security that is initiated by Bank Indonesia (BI) and the Central Statistics Agency (BPS) through Regional Inflation Control Team (TPID). This study uses FGD and AHP method are used to build the index of food security EWS. The results of this study indicate that areas that do not have high production in food commodities indeed have a higher susceptibility than having high production.
Predicting Medicine-Stocks by Using Multilayer Perceptron Backpropagation

Artificial neural network has a lot of ability in controlling the error rate to formulate some of its functions as a supervised method. Medicine is one of the major needs for each patient, so that every hospital should know how much inventory of drugs is used and needed by patients every day. This study uses artificial neural network with multilayer perceptron backpropagation as a solution for predicting drug stocks. Prediction of drug stocks using medicine prior period stock data for three years is used to get the predicted results with a small error rate. Backpropagation algorithm using the error output is used to change the weights in the backward direction. To get this error, forward stage should be done first. The results of experiments using backpropagation with the configuration of 0.04 mementum which has training rate of 0.001 gets the value of MSE of 0.00001.
Towards an Open-Domain Framework for Distilling the Outcomes of Personal Experiences from Social Media Timelines

Millions of people share details about their real-world experiences on social media. This provides an opportunity to observe the outcomes of common and critical situations and actions for individual and societal benefit. In this paper, we discuss our efforts to design and build an open-domain framework for mining the outcomes of any given experience from social media timelines. Through a number of example situations and actions across multiple domains, we discuss the kinds of outcomes we are able to extract and their relevance.
Agent Based Realtime Pedagogy For Proof Construction

There is a disturbing paradox at the heart of contemporary American education: As this education turns more and more "electronic," we are moving away from the one kind of learning that we know to be most effective, namely, one-on-one instruction.As the need for good teachers at the university level continues to grow, we see this paradox intensifying.And we see the problem manifesting itself in a particularly nasty way in curricula that predominantly focus on cultivating abstract reasoning ability in future scientists and engineers.The data tells us that as educators, we are not producing students able to successfully employ context-independent reasoning in technical domains.This is true despite the fact that there has been great progress made in developing educational technologies and aides for teaching formal, context -independent deductive reasoning; we refer here to an abundance of proof-construction environments.The fact is, teaching students to be good abstract reasoners requires the professor to have a one-on-one relationship with each student, with a keen eye on how each searches for a solution.The perfect automated logic instructor should be adaptable, and fully available to each student, at every time and every place.This is obviously not possible with human instruction, but our preliminary work suggests that our vision is capable of being realized in the digital domain: We are developing a suite of intelligent agents that bring the cutting edge in AI-based tutoring to the state-of-the-art in proof construction courseware.In addition, with agent-driven tutoring systems as a foundation, we aim to extend our agents so that they can be of assistance to logicians, mathematicians, and computer scientists in their research and development.Unfortunately, proof-construction environments in the educational realm, while presenting lucid proofs to the student, are based on weak theorem provers -provers that lack the sheer muscle to be of use to a professional scientist or engineer.We remedy the situation by using "industrial grade" theorem provers as the testbed for the development of our artificial assistants.
A Hands On Approach To Teaching Wireless Ad Hoc Networks

routing algorithms for wireless and wired networks, load-balanced adaptive routing techniques for wireless ad hoc networks, performance analysis and optimization of network parameters, rapid prototyping of autonomous robots, and network architectures for healthcare.
MML Is Not Consistent for Neyman-Scott

Strict Minimum Message Length (SMML) is an information-theoretic statistical inference method widely cited (but only with informal arguments) as providing estimations that are consistent for general estimation problems. It is, however, almost invariably intractable to compute, for which reason only approximations of it (known as MML algorithms) are ever used in practice. Using novel techniques that allow for the first time direct, non-approximated analysis of SMML solutions, we investigate the Neyman-Scott estimation problem, an oft-cited showcase for the consistency of MML, and show that even with a natural choice of prior neither SMML nor its popular approximations are consistent for it, thereby providing a counterexample to the general claim. This is the first known explicit construction of an SMML solution for a natural, high-dimensional problem.
One-variable context-free hedge automata

We introduce an extension of hedge automata called One-Variable Context-Free Hedge Automata. The class of unranked ordered tree languages they recognize has polynomial membership problem and is preserved by rewrite closure with inverse-monadic rules. We also propose a modeling of primitives of the W3C XQuery Update Facility by mean of parameterized rewriting rules, and show that the rewrite closure of a context-free hedge language with these extended rewriting systems is a context-free hedge language. This result is applied to static analysis of XML access control policies expressed with update primitives.
A Nonparametric Approach Using Dirichlet Process for Hierarchical Generalized Linear Mixed Models

In this paper, we propose a nonparametric approach using the Dirichlet processes (DP) as a class of prior distributions for the distribution G of the random effects in the hierarchical generalized linear mixed model (GLMM).The support of the prior distribution (and the posterior distribution) is large, allowing for a wide range of shapes for G.This provides great flexibility in estimating G and therefore produces a more flexible estimator than does the parametric analysis.We present some computation strategies for posterior computations involved in DP modeling.The proposed method is illustrated with real examples as well as simulations.
Service Function Path Establishment

Service Function Chain architecture leads to more adaptive networknodes. It allows splitting the network function into fine-grainedbuild blocks --- service function, and combining the network functionsinto service function chain to formulate complicated services.Further, the service function chain should be instantiated byselecting the optimal instance from the candidates for each servicefunction in it. This document discusses the required components duringthe instantiation of service function chain in the network.
TweetGrep: Weakly Supervised Joint Retrieval and Sentiment Analysis of Topical Tweets

An overwhelming amount of data is generated everyday onsocial media, encompassing a wide spectrum of topics. With almost every business decision depending on customer opinion, mining of social media data needs to be quick and easy.For a data analyst to keep up with the agility and the scale of the data, it is impossible to bank on fully supervised techniques to mine topics and their associated sentiments from social media. Motivated by this, we propose a weakly supervised approach (named, TweetGrep) that lets the data analyst easily define a topic by few keywords and adapt a generic sentiment classifier to the topic – by jointly modeling topics and sentiments using label regularization. Experiments with diverse datasets show that TweetGrep beats the state-of-the-art models for both the tasks of retrieving topical tweet sand analyzing the sentiment of the tweets (average improvement of 4.97% and 6.91% respectively in terms of area under the curve). Further, we show that TweetGrep can also be adopted in a novel task of hashtag disambiguation, which significantly outperforms the baseline methods.
A Framework for Playable Social Dialogue

Non-player characters in games generally lack believability and deep interactivity. The AI system Ensemble begins to tackle this by modeling social state and behaviors for game characters. With previous versions of Ensemble, the player initiates social exchanges and the system generates the dialogue and outcome and displays them in their entirety. In this paper we present an extension to Ensemble comprised of a model of playable social dialogue called social practices. Social practices increase the playability of character interactions and add interactivity at each stage of dialogue. Additionally, this work enables a more modular form of authoring to support the additional complexity.
Who Will Respond to Your Requests for Instant Trouble-Shooting?

Instant mentoring services are novel social media, in which mentees can input expertise requests and wait for accepting some expert mentors who are willing to tackle the requests in an instant and one-by-one manner. While mentee's satisfaction of being mentored is determined by the matched mentor, this paper aims at analyzing and finding which mentors will respond to the given request raised by a mentee in Codementor, which is one of the popular instant mentoring services. We formulate the Mentor Willingness Ranking (MWR) problem. MWR is to understand whether a mentor is willing to tackle a request. We propose to deal with the task by generating a ranked list of mentors such that those mentors who are really willing to tackle the request are as many as possible. We develop three categories of features, Availability, Capability, and Activity, to model the willingness of a mentor dealing with the request. Results of analysis show the effectiveness of these features, and encourage develop learning-based method to accurately identify the willing mentors.
ENRICH: A Query Rewriting Service Powered by Wikipedia Graph Structure

The search for relevant information in websites can be very frustrating for users who, unintentionally, use too general or inappropriate keywords to express their requests. To overcome this situation, query rewriting techniques aim at transforming the users requests to better describe the real intent of the users. However, to the best of our knowledge, current search tools either are too generic or require resources not available for everyone such as query log processors, natural language engines, etc. To supply this need, we present ENRICH, which is a query rewriting cloud service that is automatically tailored to each website and it is powered by an available, accessible and open resource: Wikipedia.
FutureTrust – Future Trust Services for Trustworthy Global Transactions

Against the background of the regulation 2014/910/EU [1] on electronic identification (eID) and trusted services for electronic transactions in the internal market (eIDAS), the FutureTrust project 1 , which is funded within the EU Framework Programme for Research and Innovation (Horizon 2020) under Grant Agreement No. 700542, aims at supporting the practical implementation of the regulation in Europe and beyond. For this purpose, the FutureTrust project will address the need for globally interoperable solutions through basic research with respect to the foundations of trust and trustworthiness, actively support the standardisation process in relevant areas, and provide Open Source software components and trustworthy services which will ease the use of eID and electronic signature technology in real world applications. The FutureTrust project will extend the existing European Trust Service Status List (TSL) infrastructure towards a “Global Trust List”, develop a comprehensive Open Source Validation Service as well as a scalable Preservation Service for electronic signatures and seals. Furthermore, it will provide components for the eID-based application for qualified certificates across borders, and for the trustworthy creation of remote signatures and seals in a mobile environment. The present contribution provides an overview of the FutureTrust project and invites further stakeholders to actively participate in contributing to the development of future trust services for trustworthy global transactions.
Integrating Of A Project Management Database For Six Sigma Quality Course

Many organizations utilize Six Sigma project management techniques to reduce cost in their operations.Engineering managers are expected to know how to manage this specialized project management force including black belt, green belt, and yellow belt project engineers in order to deliver the expected savings and efficiencies that have been produced by other Six Sigma organizations.In order to educate future engineering managers it is important to provide an environment that facilitates learning of how to manage Six Sigma initiatives.This transcript describes the testing and evaluation of the impact a project database had on teaching Total Quality Management using Six Sigma techniques.The results provide evaluation from engineering managers from industry, students in the course, and the Certified Six Sigma Black Belt committee members at the University of Nebraska-Lincoln.Results indicate that using the database provided insights on how an engineering manager would manage these specialists in this environment.
Software and System Engineering Education: Commonalities and Differences

The complexity of current engineered systems and the increasing role of software in those systems emphasize the need for educating and training qualified systems engineers to meet future demand.As the role of software increases in the overall operation and success of systems it becomes necessary for the system engineers to understand and appreciate the software engineering methods and practices.The same is true for software engineers, where they need to understand the overall complexity of the systems they deal with, and understand systems engineering methods and practices.Over the past five years, educators and professional software and systems engineers from around the world have been working on the development of two graduate reference curricula: the Graduate Software Engineering Reference Curriculum and the Graduate Reference Curriculum for Systems Engineering.This paper provides an overview of these curricula, and how they might influence the quality and effectiveness of the development of future Software Intensive Systems.In addition, the paper presents some of the commonalities and differences between software engineering and system engineering graduate education, and a discussion of the challenges involved in graduating engineers, qualified for work on software intensive systems.
A Cognitive Based Approach To The Implementation Of The Introductory Computer Science Programming Sequence

A cognitive-based approach is being used to develop comprehensive materials for the first courses in Computer Science based on Implementation D of Computing Curricula 1991.The distinguishing features are:(1) materials based on a strategic sequencing and the associated Bloom level of mastery of key topics, (2) topical coverage carefully based on a spiral approach to information presentation, (3) integral use of structured labs as a necessary component of the course, (4) an emphasis on frequent feedback to facilitate learning and to evaluate the effectiveness of instruction, (5) an early use of teams, (6) a student surveying tool used to track all students to provide outcome assessment, and (7) review and evaluation by multiple institutions for iterative material refinement and national dissemination.A preliminary on-site evaluation, by a team of five consultants with expertise in the fields of computing, computing education and educational psychology, was conducted at the beginning of the project to critique project planning and initial materials.Materials currently developed will be class tested and evaluated by other faculty during the remainder of this year.These updated materials will be refined and further disseminated.The evaluation of materials will continue with the original five onsite consultants, three off-site consultants and several review/adoption institutions.
Facilitating Active Learning Engineering Courses With Tablet Pc Note Taking Technology

This paper describes an investigation of a means to use technology to help students construct meaning through the use of the dimensions of Learning (DOL) pedagogical frame work.We use the tablet PC and specialized note taking software to facilitate the three minute pause technique and summarizing.This is done in an active learning environment and serves as a formative assessment for the class section being taught.These activities are done during the delivery of the performance tasks presented to a lower level pre-calculus course section.Details of the performance tasks, assessments and are discussed as well as the resulting impact of using tablet PCs as compared to laptops and conventional writing for note taking are discussed.Our results indicate although handwritten notes are generally preferred by most students, the tablet PC does have some advantages.Furthermore the users of the tablet PCs appeared to take better notes and benefit more from the summary exercise.