Reviewerly/exHarmony huggingface dataset:
Adapting AI Behaviors To Players in Driver San Francisco: Hinted-Execution Behavior Trees

The creative nature of games makes trying new ideas desirable, but these changes are sometimes very risky. We need to find ways to minimize risks while we build innovative experiences. Driver San Francisco did this by using Hinted-execution Behavior Trees; this technique allows developers to modify existing AI behaviors dynamically with very low risk, and was used to adapt Driver’s getaway AI to players’ skills.
A Novel Two-stage Framework for Extracting Opinionated Sentences from News Articles

This paper presents a novel two-stage framework to extract opinionated sentences from a given news article. In the first stage, Naive Bayes classifier by utilizing the local features assigns a score to each sentence - the score signifies the probability of the sentence to be opinionated. In the second stage, we use this prior within the HITS (Hyperlink-Induced Topic Search) schema to exploit the global structure of the article and relation between the sentences. In the HITS schema, the opinionated sentences are treated as Hubs and the facts around these opinions are treated as the Authorities. The algorithm is implemented and evaluated against a set of manually marked data. We show that using HITS significantly improves the precision over the baseline Naive Bayes classifier. We also argue that the proposed method actually discovers the underlying structure of the article, thus extracting various opinions, grouped with supporting facts as well as other supporting opinions from the article.
Using Connectionist Networks to Examine the Role of Prior Constraints in Human Learning

This research investigated the effects of prior knowledge on learning in psychologically-plausible connectionist networks. This issue was examined with respect to the benchmark orthography-to-phonology mapping task (Sejnowski & Rosenberg, 1986; Seidenberg & McClelland, 1989). Learning about the correspondences between orthography and phonology is a critical step in learning to read. Children (unlike the networks mentioned above) bring to this task extensive knowledge about the sound-structure of their language. We first describe a simple neural network that acquired some of this phonological knowledge. We then summarize simulations showing that having this knowledge in place facilitates the acquisition of orthographicphonological correspondences, producing a higher level of asymptotic performance with fewer implausible errors and better nonword generalization. The results suggest that connectionist networks may provide closer approximations to human performance if they incorporate more realistic assumptions about relevant sorts of background knowledge.
Understanding Public Emotional Reactions on Twitter

Social media has become an important communication medium for people to express their feelings and opinions, sometimes on a daily basis. We demonstrate a system that helps monitor and understand how people feel about specific topics or events. Using a large vocabulary of emotion words constructed from a number of public domain resources and mapping them to an emotion hierarchy, our system is able to analyse incoming posts gathered on a topic of interest and visualise the emotional pulse of a geographical region as it reacts to that event. The content analysis component of the tool then enables users to gain insights into the mood variations exhibited. We illustrate the system by exploring the public reaction to a specific recent event: the Sydney siege tragedy on December 15th, 2014.
Gibbs-type Indian Buffet Processes

We investigate a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures.Two existing classes are contained as special cases: the original two-parameter Indian buffet process, corresponding to the Dirichlet process, and the stable (or three-parameter) Indian buffet process, corresponding to the Pitman-Yor process.Asymptotic behavior of the Gibbs-type partitions, such as power laws holding for the number of latent clusters, translates into analogous characteristics for this class of Gibbs-type feature allocation models.Despite containing several different distinct subclasses, the properties of Gibbs-type partitions allow us to develop a black-box procedure for posterior inference within any subclass of models.Through numerical experiments, we compare and contrast a few of these subclasses and highlight the utility of varying power-law behaviors in the latent features.
Cultures of the Future: Emergence and Intervention in Design Anthropology

The research field of design anthropology is emerging between the fields of anthropology and design research. This chapter argues that more attention to the ways in which design anthropologists engage with the complex challenges of emergent cultural forms, and with exploring directions for possible futures. It explores some central epistemological questions concerning this kind of knowledge—transformative knowledge, created in and through action and engagement rather than by observation and reflection alone—to explore and to provoke intentional change. The chapter focuses on emergence and intervention as central concepts for design anthropology, and argues that these concepts are intrinsically connected in a dialectical movement of exploration, knowledge production and transformation. It also explores the possibilities of an anthropology that engages with emergent cultural practices as a way of producing knowledge that is part of, and itself constitutes, transformative practices.
Relativistic corrections to photonic entangled states for the space-based quantum network

In recent years there has been a great deal of focus on a globe-spanning quantum network, including linked satellites for applications ranging from quantum key distribution to distributed sensors and clocks. In many of these schemes, relativistic transformations may have deleterious effects on the purity of the distributed entangled pairs. In this paper, we make a comparison of several entanglement distribution schemes in the context of special relativity. We consider three types of entangled photon states: polarization, single photon, and Laguerre-Gauss mode entangled states. All three types of entangled states suffer relativistic corrections, albeit in different ways. These relativistic effects become important in the context of applications such as quantum clock synchronization, where high fidelity entanglement distribution is required.
An Improved Upper Bound for Bootstrap Percolation in All Dimensions

In $r$-neighbor bootstrap percolation on the vertex set of a graph $G$, a set $A$ of initially infected vertices spreads by infecting, at each time step, all uninfected vertices with at least $r$ previously infected neighbors. When the elements of $A$ are chosen independently with some probability $p$, it is natural to study the critical probability $p_c(G,r)$ at which it becomes likely that all of $V(G)$ will eventually become infected. Improving a result of Balogh, Bollobás, and Morris, we give a bound on the second term in the expansion of the critical probability when $G = [n]^d$ and $d \geq r \geq 2$. We show that for all $d \geq r \geq 2$ there exists a constant $c_{d,r} &gt; 0$ such that if $n$ is sufficiently large, then \[ p_c([n]^d, r) \leq \Biggl(\dfrac{λ(d,r)}{\log_{(r-1)}(n)} - \dfrac{c_{d,r}}{\bigl(\log_{(r-1)}(n)\bigr)^{3/2}}\Biggr)^{d-r+1}, \] where $λ(d,r)$ is an exact constant and $\log_{(k)}(n)$ denotes the $k$-times iterated natural logarithm of $n$.
Tablets For Timely Design Documentation

One of the biggest challenges we have experienced in supervising digital systems senior design projects is the quality and completeness of the individual lab notebooks.Of the five outcomes we continuously track for this capstone course, the lab notebooks have consistently received the lowest quantitative scores.A significant improvement was achieved three years ago when we transitioned from "carbon paper and pen" notebooks to on-line (HTML) notebooks.Many teams took advantage of (and put to good use) the ability to post digital pictures of prototyping setups, provide hyperlinks to all their device datasheets, post their latest schematics and software listings for evaluation, and post video clips of their project in action (as verification of their project success criteria).The primary drawback has been the need for students to be in front of a networked computer to make lab notebook entries; consequently, the notebook updates still tended to be done in "spurts" (typically "after the fact") rather than in "real time".Project work (and inspiration), in fact, does not always occur in a lab setting, where networked computers are readily available, nor does it occur when all team members are working in the same physical location.Our hypothesis is that equipping each project team with wireless Tablet PCs should not only significantly improve the spontaneity (and regularity) with which the on-line lab notebooks are updated, but also facilitate collaboration among team members working on the design project at different locations.An HP Technology for Teaching Grant has provided a critical mass of Tablet PCs to test this hypothesis.A description of how the equipment provided is being utilized, along with a discussion of the preliminary results obtained, is presented in this paper.
Overcomplete quantum tomography of a path-entangled two-photon state

Path-entangled $N$-photon states are key resources for quantum enhanced metrology and quantum imaging, as well as quantum computation. However, the quantum tomography of path-entangled indistinguishable photons is still in its infancy. We propose and implement a quantum tomographical method to characterize path-entangled two-photon NOON states, which can be extended to arbitrary $N$. To access both the populations and the coherences of the path-encoded density matrix, a single ancilla spatial mode is introduced, and photon correlations are performed as a function of a single phase within an interferometer. We characterize a two-photon state generated through the Hong-Ou-Mandel interference of indistinguishable single photons emitted by a semiconductor quantum dot and show that an overcomplete data set reveals spatial coherences that could be otherwise hidden due to limited statistics. We finally extract the truly indistinguishable part of the density matrix and identify the main origin for the reduced degree of entanglement.
Fuzzy Approach to Decision Support System Design for Inventory Control and Management

Abstract The ubiquitous nature of inventory and its reliance on a reliable decision support system (DSS) is crucial for ensuring continuous availability of goods. The DSS needs to be designed in a manner that enables it to highlight its present status. Further, the DSS should be able to provide indications about subtle and large-scale variations that are likely to occur in the supply chain within the context of the decision-making framework and inventory management. However, while dealing with the parameters of the system, it is observed that its operations and mechanisms are surrounded by uncertain, imprecise, and vague environments. Fuzzy-based approaches are best suited for such situations; however, these require assistance from learning systems like artificial neural network (ANN) to facilitate automated decision support. When ANN and fuzzy are combined, the fuzzy neural system and the neuro-fuzzy system (NFS) are formulated. The model of the DSS reported here is based on a framework commonly known as adaptive neuro-fuzzy inference system (ANFIS), which is a version of NFS. The configured model has the advantages of both the ANN and fuzzy systems, and has been tested for the design of a DSS for use as part of inventory control. In this work, we report the design of an ANFIS-based DSS configured to work as DSS for inventory management. The system accepts demand as input and generates procurement, ordering, and holding cost to control production and supply. The system deals with a certain profitability rating required to quantify the changes in the input and is combined with the day-to-day inventory records and demand-available cycle. The effectiveness of the system has been checked in terms of number and types of membership used, accuracy generated, and computational efficiency accounted by the computation cycles required.
Practically secure quantum position verification

We discuss quantum position verification (QPV) protocols in which the verifiers create and send single-qubit states to the prover. QPV protocols using single-qubit states are known to be insecure against adversaries that share a small number of entangled qubits. We introduce QPV protocols that are practically secure: they only require single-qubit states from each of the verifiers, yet their security is broken if the adversaries share an impractically large number of shared entangled qubits. These protocols are a modification of known QPV protocols in which we include a classical random oracle without altering the amount of quantum resources needed by the verifiers. We present a cheating strategy that requires a number of entangled qubits shared among the adversaries that grows exponentially with the size of the classical input of the random oracle.
Improving Terrain Analysis and Applications to RTS Game AI

This paper presents a new terrain analysis algorithm for RTS games. The proposed algorithms significantly improves the analysis time of the state of the art via contour tracing, and also offers better chokepoint detection. We demonstrate that our approach (BWTA2) is at least 10 times faster than the commonly used BWTA in a collection of StarCraft maps. Additionally, we show the usefulness of terrain analysis in tasks such as pathfinding and discuss potential applications to strategic decision making tasks.
New Key Generation and Encryption Algorithms for Privacy Preservation in Mobile Ad Hoc Networks

Mobile Ad Hoc Networks (MANETs) get widespread applications along with the evolving technologies. However, MANETs are at risk due to the shortage of security mechanisms. In this paper, we propose new algorithms for key generation and encryption for privacy preservation in MANETs. Our key generation algorithm modified Fibonacci sequence by adding scrambling factors to generate random key sequences with required length but incurred low computational overhead, whereas the encryption/decryption algorithm utilizes the One Time Pad (OTP) system by adding scrambling factors for data confidentiality which satisfies the randomness, diffusion, and confusion tests. Simulation of the proposed algorithms was conducted using Matlab and NS-2. Experiment results showed that the proposed algorithms produced random key sequences and random Ciphertexts. Through several tests i.e. speed, correlation and autocorrelation, diffusion, and confusion tests, the simulation result showed the superiority of our algorithms over the other algorithms. For the proof of concept, the proposed algorithms have been simulated in the network simulator, where the result showed that along with the increase of the number of nodes, the throughput of the network increased, while the delay is relatively constant around 6000 ms for 20 up to 70 nodes.
Sequential dimension reduction for learning features of expensive black-box functions

Learning a feature of an expensive black-box function (optimum, contour line,...) is a difficult task when the dimension increases. A classical approach is two-stage. First, sensitivity analysis is performed to reduce the dimension of the input variables. Second, the feature is estimated by considering only the selected influential variables. This approach can be computationally expensive and may lack flexibility since dimension reduction is done once and for all. In this paper, we propose a so called Split-and-Doubt algorithm that performs sequentially both dimension reduction and feature oriented sampling. The 'split' step identifies influential variables. This selection relies on new theoretical results on Gaussian process regression. We prove that large correlation lengths of covariance functions correspond to inactive variables. Then, in the 'doubt' step, a doubt function is used to update the subset of influential variables. Numerical tests show the efficiency of the Split-and-Doubt algorithm.
Anfis Classifier Based Moving Object Detection and Segmentation in Indoor and Outdoor Environments

Background: Moving object detection in dynamic environment video is more complex than the static environment videos. In this paper, moving objects in video sequences are detected and segmented using feature extraction based Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier approach. The proposed moving object detection methodology is tested on different video sequences in both indoor and outdoor environments. Methods: This proposed methodology consists of background subtraction and classification modules. The absolute difference image is constructed in background subtraction module. The features are extracted from this difference image and these extracted features are trained and classified using ANFIS classification module. Results: The proposed moving object detection methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure. The proposed moving object segmentation methodology is executed on different Central Processing Unit (CPU) processor as 1.8 GHz and 2.4 GHz for evaluating the performance during moving object segmentation. At present, some moving object detection systems used 1.8 GHz CPU processor. Recently, many systems for moving object detection are using 2.4 GHz CPU processor. Hence, CPU processors 1.8 GHz and 2.4 GHz are used in this paper for detecting the moving objects in video sequences. Table 1 shows the performance evaluation of proposed moving object detection on CPU processor 1.8 GHz (100 sequence). Table 2 shows the performance evaluation of proposed moving object detection on CPU processor 2.8 GHz (100 sequence). The average moving object detection time on CPU processor 1.8 GHz for fountain sequence is 62.5 seconds, for airport sequence is 64.7 seconds, for meeting room sequence is 71.6 seconds and for Lobby sequence is 73.5 seconds, respectively, as depicted in Table 3. The average elapsed time for moving object detection on 100 sequences is 68.07 seconds. The average moving object detection time on CPU processor 2.4 GHz for fountain sequence is 56.5 seconds, for airport sequence is 54.7 seconds, for meeting room sequence is 65.8 seconds and for Lobby sequence is 67.5 seconds, respectively, as depicted in Table 4. The average elapsed time for moving object detection on 100 sequences is 61.12 seconds. It is very clear from Table 3 and Table 4; the moving object detection time is reduced when the frequency of the CPU processor increases. Conclusion: In this paper, moving object is detected and segmented using ANFIS classifier. The proposed method initially segments the background image and then features are extracted from the threshold image. These features are trained and classified using ANFIS classification method. The proposed moving object detection method is tested on different video sequences which are obtained from different indoor and outdoor environments. The performance of the proposed moving object detection and segmentation methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure.
A joint model for multiple dynamic processes and clinical endpoints: Application to Alzheimer's disease

As other neurodegenerative diseases, Alzheimer's disease, the most frequent dementia in the elderly, is characterized by multiple progressive impairments in the brain structure and in clinical functions such as cognitive functioning and functional disability. Until recently, these components were mostly studied independently because no joint model for multivariate longitudinal data and time to event was available in the statistical community. Yet, these components are fundamentally interrelated in the degradation process toward dementia and should be analyzed together. We thus propose a joint model to simultaneously describe the dynamics of multiple correlated components. Each component, defined as a latent process, is measured by one or several continuous markers (not necessarily Gaussian). Rather than considering the associated time to diagnosis as in standard joint models, we assume diagnosis corresponds to the passing above a covariate‐specific threshold (to be estimated) of a pathological process that is modeled as a combination of the component‐specific latent processes. This definition captures the clinical complexity of diagnoses such as dementia diagnosis but also benefits from simplifications for the computation of maximum likelihood estimates. We show that the model and estimation procedure can also handle competing clinical endpoints. The estimation procedure, implemented in an R package, is validated by simulations and the method is illustrated on a large French population‐based cohort of cerebral aging in which we focused on the dynamics of three clinical manifestations and the associated risk of dementia and death before dementia.
Accurate Computation of Conditional Expectation for Highly Nonlinear Problems

This paper focuses on inverse problems to identify parameters by incorporating information from measurements. These generally ill-posed problems are formulated here in a probabilistic setting based on Bayes's theorem because it leads to a unique solution of the updated distribution of parameters. Many approaches build on Bayesian updating in terms of probability measures or their densities. However, the uncertainty propagation problems and their discretization within the stochastic Galerkin or collocation method are naturally formulated for random vectors, which calls for updating of random variables, i.e., a filter. Such filters typically build on some approximation to conditional expectation (CE). Specifically, the approximation of the CE with affine functions leads to the familiar Gauss--Markov--Kalman filter, which works best on linear or close to linear problems only. Our approach builds on a reformulation, which allows us to localize the operator of the CE to the point of measured value. The resulting conditioned expectation (CdE) predicts correctly the quantities of interest, e.g., conditioned mean and covariance, even for general highly nonlinear problems. The novel CdE allows straightforward numerical integration; particularly, the approximated covariance matrix is always positive definite for integration rules with positive weights. The theoretical results are confirmed by numerical examples.
Constructing TI-Friendly Substitution Boxes Using Shift-Invariant Permutations

The threat posed by side channels requires ciphers that can be efficiently protected in both software and hardware against such attacks. In this paper, we proposed a novel Sbox construction based on iterations of shift-invariant quadratic permutations and linear diffusions. Owing to the selected quadratic permutations, all of our Sboxes enable uniform 3-share threshold implementations, which provide first order SCA protections without any fresh randomness. More importantly, because of the "shift-invariant" property, there are ample implementation trade-offs available, in software as well as hardware. We provide implementation results (software and hardware) for a four-bit and an eight-bit Sbox, which confirm that our constructions are competitive and can be easily adapted to various platforms as claimed. We have successfully verified their resistance to first order attacks based on real acquisitions. Because there are very few studies focusing on software-based threshold implementations, our software implementations might be of independent interest in this regard.