Reviewerly/exHarmony huggingface dataset:
A Simplistic Approach To Reactive Multi Robot Navigation In Unknown Environments

Multi-agent control is a very promising area of robotics.In applications for which it is difficult or impossible for humans to intervene, the utilization of multi-agent, autonomous robot groups is indispensable.In this paper, a novel approach to multi-agent control is analyzed in which concepts from several different engineering disciplines are unified within a simplistic yet robust framework.While many recently researched strategies for multi-agent control require cumbersome calculations and complex sensor layouts, the approach presented here enables a multiagent group to perform very accurate navigational tasks using minimal sensory equipment and simple geometrical calculations.This approach incorporates various concepts from geometry, trigonometry, sensor fusion, software/hardware integration, and RF communication in order to explore a method that can be used to simplify the overall process of coordinating multi-robot navigation in unknown (unmapped) environments.Further, the purpose of this paper is to present a possible new direction for engineering education in the area of multi-agent robotics.Simplicity is one of the most important aspects of a multi-agent control scheme.Many recently researched strategies to multi-agent control tend to increase the complexity of their respective systems for the purpose of attaining superior results.The research presented here utilizes the concept of Occam's Razor or the principle of parsimony, which states: "It is vain to do with more what can be done with less."This concept is extremely profound from an engineering standpoint, and engineering education would be greatly enhanced by emphasizing this axiom.In this paper, a multi-agent robot group is shown to demonstrate extremely efficient navigational patterns based upon a control scheme that incorporates the concept of Occam's Razor.The fundamental idea upon which this approach is based is that a multi-robot team can cooperate to determine the shortest path through an unknown environment given only a very simple set of rules.In addition, the approach being presented in this paper can be successfully implemented using very simple sensors.The idea was implemented with two robots.In simulation, groups consisting of over sixty agents were tested, and it was shown that a near optimal (shortest) path emerges through the environment without mapping the environment.The experimental results clearly show that the robots' navigational ability through various unknown environments improves with time under the control of this algorithm.Furthermore, the simplicity of this approach makes implementation very practical and easily expandable to reliably control a group Page 8.116.
Image Encryption Using Permutation and Hill Cipher

Encryption is used to securely transmit data in open networks. Each type of data has its own features; therefore different techniques should be used to protect confidential image data from unauthorized access. This paper, has been proposed new encryption algorithm using two different images, one is cover image which acts as key image which is shared by both sender and receiver and other is Informative image. As first step, XOR cover image with informative image to obtain resultant image. The resultant image is decomposed into (n x n) blocks which passed to the Hill Cipher algorithm to form encrypted blocks. The encrypted blocks are transformed into new locations using permutation table.
The Quality of Online Answers to Parents Who Suspect That Their Child Has an Autism Spectrum Disorder

The growing diagnosis and public awareness of Autism Spectrum Disorders (ASD) leads more parents to seek answers to their suspicions for ASD in their child on Internet forums. This study describes an analysis of the quality of content of 371 answers on Yahoo Answers (YA), a social question-and-answer forum, to parents querying whether their child has ASD. We contrasted the perceived quality of answers by clinicians with that of parents. The study tested the feasibility of automatically assisting parents in selecting answers with higher quality using a predictive model based on the text of answers and the attributes of answerers.
Building Engineers One Posting At A Time: Social Networking For Recruiting Engineering Majors

As social networking continues to gain in popularity, essential functions traditionally assigned to electronic mail, instant messaging and conventional websites are slowly being subsumed by, and coalesced within, social networking sites such as Facebook and MySpace.These sites offer what are basically personalized and enhanced versions of both electronic mail and static content in a user-created digest form and thus represent a significant departure from the most familiar electronic information formats.During the recruiting season for a recent incoming class of civil and mechanical engineers, the authors made a significant effort to reduce the distance between information about our programs, the profession, members of the profession and students by creating a significant presence for the department on a popular social networking site.This paper presents the outreach goals of the initiative, the nuts-and-bolts of how it was executed, the effect on recruiting and student excitement and, lastly, the author's observations and recommendations for similar efforts.
A Study Of Mainstream Features Of Crm System And Evaluation Criteria

This paper will discuss an overview of Customer Relationship Management (CRM) systems, mainstream CRM features and functionalities, CRM evaluation criteria and vendors selection.It will also include selecting a piece of enterprise application software, resolving a conflict of vendor solution and client's requirements.The paper provides the study of the CRM features and evaluation criteria is based on a small to medium manufacturing company.A comparison table was prepared for helping the company to select an appropriate vendor.
Expertise in Social Networks: How Do Experts Differ from Other Users?

Expertise location is a difficult task, with expertise often being implied and liable to change. In this paper we propose a heuristic-based approach for automated identification of expertis eon Twitter. We collect tweets from experts and non-experts in different domains and compute different types of features based on the heuristics regarding properties of the messages written and re-tweeted by the experts. We show that these heuristics provide us with interesting insights regarding how experts differ from other user groups which can help guide future studies in this areas and algorithms for expertise location.
Rock, Paper, StarCraft: Strategy Selection in Real-Time Strategy Games

The correct choice of strategy is crucial for a successful real-time strategy (RTS) game player. Generally speaking, a strategy determines the sequence of actions the player will take in order to defeat his/her opponents. In this paper we present a systematic study of strategy selection in the popular RTS game StarCraft. We treat the choice of strategy as a game itself and test several strategy selection techniques, including Nash Equilibrium and safe opponent exploitation. We adopt a subset of AIIDE 2015 StarCraft AI tournament bots as the available strategies and our results suggest that it is useful to deviate from Nash Equilibrium to exploit sub-optimal opponents on strategy selection, confirming insights from computer rock-paper-scissors tournaments.
An Empirical Evaluation of Evaluation Metrics of Procedurally Generated Mario Levels

There are several approaches in the literature for automatically generating Infinite Mario Bros levels. The evaluation of such approaches is often performed solely with computational metrics such as leniency and linearity. While these metrics are important for an initial exploratory evaluation of the content generated, it is not clear whether they are able to capture the player's perception of the content generated. In this paper we evaluate several of the commonly used computational metrics. Namely, we perform a systematic user study with procedural content generation systems and compare the insights gained from our user study with those gained from analyzing the computational metric values. The results of our experiment suggest that current computational metrics should not be used in lieu of user studies for evaluating content generated by computer programs.
Improving Monte Carlo Tree Search Policies in StarCraft via Probabilistic Models Learned from Replay Data

Applying game-tree search techniques to RTS games poses a significant challenge, given the large branching factors involved. This paper studies an approach to incorporate knowledge learned offline from game replays to guide the search process. Specifically, we propose to learn Naive Bayesian models predicting the probability of action execution in different game states, and use them to inform the search process of Monte Carlo Tree Search. We evaluate the effect of incorporating these models into several Multiarmed Bandit policies for MCTS in the context of StarCraft, showing a significant improvement in gameplay performance.
PAC-Bayes and domain adaptation

We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different, but related, target distribution. Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk. While this bound stands in the spirit of common domain adaptation works, we derive a second bound (introduced in Germain et al., 2016) that brings a new perspective on domain adaptation by deriving an upper bound on the target risk where the distributions' divergence-expressed as a ratio-controls the trade-off between a source error measure and the target voters' disagreement. We discuss and compare both results, from which we obtain PAC-Bayesian generalization bounds. Furthermore, from the PAC-Bayesian specialization to linear classifiers, we infer two learning algorithms, and we evaluate them on real data.
Memory Bounded Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is an effective decision making algorithm that often works well without domain knowledge, finding an increasing application in commercial mobile and video games. A promising application of MCTS is creating AI opponents for board and card games, where Information Set MCTS (ISMCTS) can provide a challenging opponent and reduces the cost of creating game-specific AI opponents. Most research to date has aimed at improving the quality of decision making by (IS)MCTS, with respect to time usage. Memory usage is also an important constraint in commercial applications, particularly on mobile platforms or when there are many AI agents. This paper presents the first systematic study of memory bounding techniques for (IS)MCTS. (IS)MCTS is well known to be an anytime algorithm. We also introduce an anyspace version of (IS)MCTS which can make effective use of any pre-specified amount of memory. This algorithm has been implemented in a commercial version of the card game Spades downloaded more than 6 million times. We find that for games of imperfect information high quality decisions can be made with rather small memory footprints, making (IS)MCTS an even more attractive algorithm for commercial game implementations.
Blockchain systems for trade clearing

Purpose This paper aims to apply blockchains (BCs) for trade clearing and settlement in a realistic clearinghouse. The purpose is to demonstrate the feasibility and scalability of this approach. Design/methodology/approach The study uses account BCs and trading BCs as building blocks for trade clearing and settlement. Careful design is made to ensure that this approach is feasible and scalable. Findings A design has been proposed that can process hundreds of thousands of trades for a clearinghouse and it addresses performance, privacy and scalability of realistic trade clearing and settlement. The design has been implemented and experimented in a clearinghouse for over two months and processes over 3B real transactions from an exchange. The first month was to experiment with the system with historical data, the second month was to experiment with real-time data during market trading hours. The system performed as designed and intended. Research limitations/implications This is the first large research paper that applied BCs for clearing in the world. The authors applied the system to a clearinghouse and processed over 3 billion transactions, equivalent to 13 years of London Stock Exchange transaction volume, demonstrating that BCs can handle a large number of transactions. Practical implications The design can be duplicated to many clearinghouses in the world, and this also paves the way BCs can be used in large financial institutions. Social implications An implication is that other trading firms, clearinghouses and banks can apply the same technology for trade clearing, ushering the way BCs can be used in institutions. As clearing is a core function in business transactions, this has significant implications. The design can be discussed and improved in various communities. Originality/value As this is the first application of BCs to large clearinghouses that uses unique BC designs. This has significant value. Many studies have been performed but few have been reported in the scientific community. The system has been implemented, experimented and demonstrated in public for months.
COGNITIVE SCIENCE: FROM MULTIDISCIPLINARITY TO INTERDISCIPLINARITY

Cognitive science is a network of interrelated scientific disciplines engaged in researching human cognition and its brain mechanisms. The birth of cognitive science has been the result of numerous integrated processes. Cognitive science is made up of experimental psychology cognition, philosophy consciousness, neuroscience, cognitive anthropology, linguistics, computer science and artificial intelligence. In recent years, a number of other research areas have been added to the body of cognitive science. Among researchers there have been discussions about whether cognitive science is a separate research area or it consists of a series of specialized areas. In fact, the point at issue is whether cognitive science is still a multidisciplinary project or already an interdisciplinary one. P. Thagard believes that cognitive science has reached the level of interdisciplinarity and explains the advances in this area through the metaphor of “trading zones”. The success elements of cognitive science are: fruitful unification of scientific interests of cognitive science founders; organizational structure of the scientific community – universities, where a special interdisciplinary intellectual environment has been created; a large number of joint research projects supported by governments and business; integrated use of scientific methods and fundamental ideas. D. Sperber and J. Miller prefer to talk not about a unified cognitive science but cognitive sciences, i.e., the commonwealth of sciences working together on the study of a single object - human cognition, however, the extent of their interactive communication is still small. Thus, we should speak about multidisciplinarity rather than genuine interdisciplinarity of the joint research of separate sciences.
Mechanical synthesis of sorting algorithms for binary trees by logic and combinatorial techniques

We develop logic and combinatorial methods for automating the generation of sorting algorithms for binary trees, starting from input-output specifications and producing conditional rewrite rules. The main approach consists in proving (constructively) the existence of an appropriate output from every input. The proof may fail if some necessary sub-algorithms are lacking. Then, their specifications are suggested and their synthesis is performed by the same principles. Our main goal is to avoid the possibly prohibitive cost of pure resolution proofs by using a natural-style proving in which domain-specific strategies and inference steps lead to a significant increase of efficiency. In addition to classical techniques for natural-style proving, we introduce novel ones (priority of certain types of assumptions, transformation of elementary goals into conditions, special criteria for decomposition of the goal and of the assumptions), as well as methods based on the properties of domain-specific relations and functions. In particular, we use combinatorial techniques in order to generate possible witnesses, which in certain cases lead to the discovery of new induction principles. From the proof, the algorithm is extracted by transforming inductive proof steps into recursions, and case-based proof steps into conditionals. The approach is demonstrated in parallel using the Theorema system, by developing the theory, implementing the prover, and performing the proofs of the necessary properties and synthesis conjectures. It is also validated in the Coq system, which allows to compare the facilities of the two systems from the point of view of our application.
The utility of statistical analysis in structural geology

Recent advances in statistical methods for structural geology make it possible to treat nearly all types of structural geology field data. These methods provide a way to objectively test hypotheses and to quantify uncertainty, and their adoption into standard practice is important for future quantitative analysis in structural geology. We outline an approach for structural geologists seeking to incorporate statistics into their workflow using examples of statistical analyses from two locations within the western Idaho shear zone. In the West Mountain location, we test the published interpretation that there is a bend in the shear zone at the kilometer scale. Directional statistics on foliations corroborate this interpretation, while orientation statistics on foliation-lineation pairs do not. This discrepancy leads us to reconsider an assumption made in the earlier work. In the Orofino location, we present results from a full statistical analysis of foliation-lineation pairs, including data visualization, regressions, and inference. These results agree with thermochronological evidence that suggests that the Orofino area comprises two distinct, subparallel shear zones. The R programming language scripts that were used for both statistical analyses can be downloaded to reproduce the statistical analyses of this paper.
Nonparametric Inference for Markov Processes with Missing Absorbing State

This paper deals with the issue of nonparametric estimation of the transition probability matrix of a non-homogeneous Markov process with finite state space and partially observed absorbing state. We impose a missing at random assumption and propose a computationally efficient nonparametric maximum pseudolikelihood estimator (NPMPLE). The estimator depends on a parametric model that is used to estimate the probability of each absorbing state for the missing observations based, potentially, on auxiliary data. For the latter model we propose a formal goodness-of-fit test based on a residual process. Using modern empirical process theory we show that the estimator is uniformly consistent and converges weakly to a tight mean-zero Gaussian random field. We also provide methodology for simultaneous confidence band construction. Simulation studies show that the NPMPLE works well with small sample sizes and that it is robust against some degree of misspecification of the parametric model for the missing absorbing states. The method is illustrated using HIV data from sub-Saharan Africa to estimate the transition probabilities of death and disengagement from HIV care.
Quantum e-commerce: a comparative study of possible protocols for online shopping and other tasks related to e-commerce

A set of quantum protocols for online shopping is proposed and analyzed to establish that it is possible to perform secure online shopping using different types of quantum resources. Specifically, a single photon based, a Bell state based and two 3-qubit entangled state based quantum online shopping schemes are proposed. The Bell state based scheme, being a completely orthogonal state based protocol, is fundamentally different from the earlier proposed schemes which were based on conjugate coding. One of the 3-qubit entangled state based scheme is build on the principle of entanglement swapping which enables us to accomplish the task without transmission of the message encoded qubits through the channel. Possible ways of generalizing the entangled state based schemes proposed here to the schemes which use multiqubit entangled states is also discussed. Further, all the proposed protocols are shown to be free from the limitations of the recently proposed protocol of Huang et al. (Quantum Inf. Process. 14, 2211-2225, 2015) which allows the buyer (Alice) to change her order at a later time (after initially placing the order and getting it authenticated by the controller). The proposed schemes are also compared with the existing schemes using qubit efficiency.
Quantum metrology out of equilibrium

We address open quantum systems out-of-equilibrium as effective quantum probes for the characterisation of their environment. We discuss estimation schemes for parameters driving a de-phasing evolution of the probe and then focus on qubits, establishing a relationship between the quantum Fisher information and the residual coherence of the probe. Finally, we apply our results to the characterisation of the ohmicity parameter of a bosonic environment.
Dynamic Compositionality in Recursive Neural Networks with Structure-Aware Tag Representations

Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing. We present a novel RvNN architecture that can provide dynamic compositionality by considering comprehensive syntactic information derived from both the structure and linguistic tags. Specifically, we introduce a structure-aware tag representation constructed by a separate tag-level tree-LSTM. With this, we can control the composition function of the existing wordlevel tree-LSTM by augmenting the representation as a supplementary input to the gate functions of the tree-LSTM. In extensive experiments, we show that models built upon the proposed architecture obtain superior or competitive performance on several sentence-level tasks such as sentiment analysis and natural language inference when compared against previous tree-structured models and other sophisticated neural models.