Reviewerly/exHarmony huggingface dataset:
Integrating Gold-Backed Cryptocurrency for Blockchain Net Settlement to Achieve Future Economic Stability

The purpose of this research is to study the possibility of Gold-backed cryptocurrency for net settlement through blockchain technology and its implications on economic globally. The paper attempts to explore the challenges in expanding cryptocurrency backed by gold and to develop the new framework on cryptocurrency backed by gold model for net settlement through blockchain technology to achieve economic stability. To achieve the objective of the study qualitative method has been used, such as content analysis and semi-structured interviews. The study is expected that by implementing a gold-backed cryptocurrency will boost intra-country trade. This effect would be more robust than those generated by the existing economic model. An Islamic monetary system in the modern digital era, would modify international monetary relations in the direction of establishing a stable and just global monetary system by merging the Gold-backed cryptocurrency model for blockchain net settlement with an Islamic fiscal policy. The implementation of gold-backed cryptocurrency through blockchain would solve many of economic problems such as inflation, change the value of money, bring stability and sustainability to our financial obligations and transactions.
Adding Smart Opponents to a First-Person Shooter Video Game through Evolutionary Design

We demonstrate how a first-person shooter (FPS) video game can be made more fun and challenging by replacing the hard-wired behavior of opponents with behaviors evolved via an evolutionary algorithm. Using the opensource FPS game Cube as a platform, we replaced the agents' (opponents) hard-wired behavior with binary DNA supporting a much richer variety of agent responses. Survival-of-the-fittest ensured that only those agents whose DNA allowed them to avoid being killed by the human player would continue on to the next "generation" (game). Mutating the DNA of the survivors provided enough variability in behavior to make the agent's actions unpredictable. Our demo will show how this approach produces an increasingly challenging level of play, more fine-tuned to the skills of an individual human player than the traditional approach using pre-programmed levels of difficulty or simply adding more opponents.
Intuitive Use of Tangible Toys

Interfaces for children have continued to evolve in terms of complexity, with toys ranging from traditional tangible interfaces to apps with digital interfaces and hybrid toys with mixed physical and digital interfaces. However, there is limited research done to investigate their potential for intuitive use. This research study compares a tangible toy and an equivalent toy in the digital world (app) for intuitive use. Non-parametric Mann-Whitney U test results showed that the tangible toy was more intuitive than the intangible counterpart. Tangible systems are less complex to use and they require less time to encode and retrieve associated knowledge to use them intuitively. They are associated with low domain transfer distance and easily discoverable features. Intangible interfaces, on the other hand, require greater complexity and time to encode and retrieve associated experiential knowledge. Intangibles are associated with larger domain transfer distance and undiscoverable features which affects their intuitive use. Design implications and future work are discussed, emphasising the need for investigating aspects that make tangible systems intuitive to use. Keywords: Intuitive Interaction; Tangibles; Intangibles; Sensorimotor
Ontological Knowledge for Goal-Driven Autonomy Agents in Starcraft

Starcraft, a commercial Real-Time Strategy (RTS) game that has enjoyed world-wide popularity (including televised professional matches), is a challenging domain for automated computer agents. Evidence of this difficulty comes not only from characteristics of the game (massive state space, stochastic actions, partial visibility, etc.) but also from three years of competitive entries in tournaments (i.e. AIIDE Annual Starcraft Competition) in which the best automated entry performs poorly against a human expert. We are interested in taking a new research direction: using semantic knowledge, such as description logic, to represent the game state with abstract concepts in order to perform high level actions.
Episodic Memory: A Final Frontier (Abbreviated Version)

A major limitation of today's computer games is the shallowness of interactions with non-player characters. To build up relationships with players, NPCs should be able to remember shared experiences, including conversations, and shape their responses accordingly. We believe that progress in AI has already reached the point where research on using NLP and large KBs in games could lead to important new capabilities. We describe our Listener Architecture for conversational games, which has been implemented in a toolkit used to make short experimental games. Episodic memory plays a central role, using analogical reasoning over a library of previous conversations with the player. Examples and scale-up issues are discussed.
Sequence Learning by Backward Chaining in Synthetic Characters

Many learning algorithms concern themselves with learning from large amounts of data without human interaction. Synthetic characters that interact with human beings present a wholly different problem: they must learn quickly from few examples provided by a non-expert teacher. Training must be intuitive, provide feedback, and still allow training of nontrivial new behaviours. We present a learning mechanism that allows an autonomous synthetic character to learn sequences of actions from natural interaction with a human trainer. The synthetic character learns from only a handful of training examples, in a realtime and complex environment. Building on an existing framework for training a virtual dog to perform single actions on command and explore its action and state space, we give the dog the ability to notice consistent reward patterns that follow sequences of actions. Using an approximate online algorithm to check the Markov property for an action, the dog can discover action sequences that reliably predict rewards and turn these sequences into actions, allowing them to be associated with speech commands. This framework leads to a natural and easy training procedure that is a version of Backward Chaining, a technique commonly used by animal trainers to teach sequences of actions.
Instructions to Contributors

-The abstract follows the author's name and address and should begin on page 1. It should state the major points of the paper as clearly and concisely as possible without the need for reference to the text and without citation of references. The heading ABSTRACT should be indented, styled in capital and small capital letters, and followed by a colon, e.g., ABSTRACT: I performed an experiment to test the hypothesis that ... I performed an experiment to test the hypothesis that ...
IDtension – Highly Interactive Drama

IDtension is an Interactive Drama project initiated 10 years ago. It provides long term solutions to the problem of combining narrativity and interactivity. The demo consists of a playable text-based interactive drama called "The Mutiny". The user is given many possibilities of meaningful narrative actions (around 100 in the middle of the story), which are fully interpreted by the narrative engine to generate story events on the fly. IDtension is based on a narrative-centered goal structure, second order narrative predicates and a model of the user. Actions are selected according to an innovative history-based interface.
Building Contextual Anchor Text Representation using Graph Regularization

Anchor texts are useful complementary description for target pages, widely applied to improve search relevance. The benefits come from the additional information introduced into document representation and the intelligent ways of estimating their relative importance. Previous work on anchor importance estimation treated anchor text independently without considering its context. As a result, the lack of constraints from such context fails to guarantee a stable anchor text representation. We propose an anchor graph regularization approach to incorporate constraints from such context into anchor text weighting process, casting the task into a convex quadratic optimization problem. The constraints draw from the estimation of anchor-anchor, anchor-page, and page-page similarity. Based on any estimators, our approach operates as a post process of refining the estimated anchor weights, making it a plug and play component in search infrastructure. Comparable experiments on standard data sets (TREC 2009 and 2010) demonstrate the efficacy of our approach.
PROMOTING AUTONOMOUS LEARNING THROUGH ‘HOT POTATOES’

This study was designed to investigate the role of „Hot Potatoes‟ in promoting autonomous learning of the English education students at Muhammadiyah University of Yogyakarta. This study was conducted on some reasons: first, autonomous learning is very significant in the success of learners‟ learning, and to make the learners implement autonomous learning is quite tricky. Second, the use of Hot Potatoes as the software is very simple and fun to publish any education materials online, and as a result, the students like the program. Therefore, it is very challenging for the teacher to find a way to promote autonomous learning to the students through the use of a simple education software called „Hot Potatoes‟. The study was aimed at exploring the roles of Hot Potatoes in English teaching and learning. Further, it also explored the advantages of the Hot Potatoes in developing the students‟ ability or habit in having autonomy in their learning. This research was a qualitative case study in nature since it dealt with a holistic description. The setting of the research was Muhammadiyah University of Yogyakarta with 10 students who have implemented Hot Potatoes in designing teaching materials for them to use at schools. The procedure to collect the data was through questionnaires and interviews with the participants. In analyzing the data, the researcher first categorized the data gained from the questionnaires and then extracted the information from the data collected through the interview process. The data were then grouped into categories and interpreted in accordance with the research questions. The study found that students first had awareness to be better in mastering English due to their online publishing the teaching materials developed with Hot Potatoes. They were aware that by publishing materials online, people all around the globe would have a chance to stop by and read the materials. This awareness led them to learn by themselves on the grammar of the sentences they wrote. As a result, they started looking up grammar books to check if the grammar was appropriate or not. The same process also happened to the choice of words and the spelling of words used in the teaching materials they developed. They checked and rechecked until they were sure of the correctness of the dictions and the spelling. The last but not least is about the cultural issue in the sentences. It dealt with the appropriateness of the sentences they make due to native speakers‟ cultural background. Therefore, the writer concluded that the use of Hot Potatoes promoted autonomy in students‟ learning.
Guided Tour Of Generalized Functions In Signal Processing

The paper considers generalized functions as a necessary ingredient in various signalprocessing techniques.Oftentimes generalized functions are implemented in a casual way and not reflecting the need to establish results on a mathematical platform.A few of the important connections between the applications and mathematical foundations are included together with several illustrations.
Towards Discovering What Patterns Trigger What Labels

In many real applications, especially those involving data objects with complicated semantics, it is generally desirable to discover the relation between patterns in the input space and labels corresponding to different semantics in the output space. This task becomes feasible with MIML (Multi-Instance Multi-Label learning), a recently developed learning framework, where each data object is represented by multiple instances and is allowed to be associated with multiple labels simultaneously. In this paper, we propose KISAR, an MIML algorithm that is able to discover what instances trigger what labels. By considering the fact that highly relevant labels usually share some patterns, we develop a convex optimization formulation and provide an alternating optimization solution. Experiments show that KISAR is able to discover reasonable relations between input patterns and output labels, and achieves performances that are highly competitive with many state-of-the-art MIML algorithms.
Challenges of adopting agile methods in a public organization

Agile development methods are widely used among business enterprises. Since the introduction of the Agile Manifesto in 2001, several agile methods have been implemented, first in single-team set-ups and later in larger multi-team set-ups for complex Information Technology (IT) system development. However, the adoption of agile methods has been slow in the public sector. This is also reflected in the academic literature, as there are only a few studies discussing agile adoption in public organizations. This paper contributes to research on the use of agile practices specifically in the context of public organizations, and sheds light on the challenges a public organization may face while adopting these practices. The aim of this paper is to identify and categorize the challenges that may hinder efficient adoption and use of agile methods in public IT projects that include private software vendors. This research is based on a case study of a large governmental office. As a result, this paper presents several categories of identified challenges, the root causes of these challenges, and a discussion of the characteristics of these challenges for the public sector.
A Real-Time and Fully Distributed Approach to Motion Planning for Multirobot Systems

Motion planning is one of the most critical problems in multirobot systems. The basic target is to generate a collision-free trajectory for each robot from its initial position to the target position. In this paper, we study the trajectory planning for the multirobot systems operating in unstructured and changing environments. Each robot is equipped with some sensors of limited sensing ranges. We propose a fully distributed approach to planning trajectories for such systems. It combines the model predictive control (MPC) strategy and the incremental sequential convex programming (iSCP) method. The MPC framework is applied to detect the local running environment real-timely with the concept of receding horizon. For each robot, a nonlinear programming is built in its current prediction horizon. To construct its own optimization problem, a robot first needs to communicate with its neighbors to retrieve their current states. Then, the robot predicts the neighbors' future positions in the current horizon and constructs the problem without waiting for the prediction information from its neighbors. At last, each robot solves its problem independently via the iSCP method such that the robot can move autonomously. The proposed method is polynomial in its computational complexity.
Privacy-aware smart city: A case study in collaborative filtering recommender systems

Ensuring privacy in recommender systems for smart cities remains a research challenge, and in this paper we study collaborative filtering recommender systems for privacy-aware smart cities. Specifically, we use the rating matrix to establish connections between a privacy-aware smart city and k-coRating, a novel privacy-preserving rating data publishing model. First, we model privacy concerns in a smart city as the problem of privacy-preserving collaborative filtering recommendation. Then, we introduce k-coRating to address privacy concerns in published rating matrices, by filling the null ratings with predicted scores. This allows us to mask the original ratings to preserve k-anonymity-like data privacy, and enhance data utility (quantified using prediction accuracy in this paper). We show that the optimal k-coRated mapping is an NP-hard problem and design an efficient greedy algorithm to achieve k-coRating. We then demonstrate the utility of our approach empirically.
Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion

We introduce a methodology for nonlinear inverse problems using a variational Bayesian approach where the unknown quantity is a spatial field. A structured Bayesian Gaussian process latent variable model is used both to construct a low-dimensional generative model of the sample-based stochastic prior as well as a surrogate for the forward evaluation. Its Bayesian formulation captures epistemic uncertainty introduced by the limited number of input and output examples, automatically selects an appropriate dimensionality for the learned latent representation of the data, and rigorously propagates the uncertainty of the data-driven dimensionality reduction of the stochastic space through the forward model surrogate. The structured Gaussian process model explicitly leverages spatial information for an informative generative prior to improve sample efficiency while achieving computational tractability through Kronecker product decompositions of the relevant kernel matrices. Importantly, the Bayesian inversion is carried out by solving a variational optimization problem, replacing traditional computationally-expensive Monte Carlo sampling. The methodology is demonstrated on an elliptic PDE and is shown to return well-calibrated posteriors and is tractable with latent spaces with over 100 dimensions.
An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs

Non-negative Matrix Factorization (NMF) plays an important role in many data mining applications for low-rank representation and analysis. Due to the sparsity that is caused by missing information in many high-dimension scenes, e.g., social networks or recommender systems, NMF cannot mine a more accurate representation from the explicit information. Manifold learning can incorporate the intrinsic geometry of the data, which is combined with a neighborhood with implicit information. Thus, manifold-regularized NMF (MNMF) can realize a more compact representation for the sparse data. However, MNMF suffers from (a) the forming of large-scale Laplacian matrices, (b) frequent large-scale matrix manipulation, and (c) the involved K-nearest neighbor points, which will result in the over-writing problem in parallelization. To address these issues, a single-thread-based MNMF model is proposed on two types of divergence, i.e., Euclidean distance and Kullback–Leibler (KL) divergence, which depends only on the involved feature-tuples' multiplication and summation and can avoid large-scale matrix manipulation. Furthermore, this model can remove the dependence among the feature vectors with fine-grain parallelization inherence. On that basis, a CUDA parallelization MNMF (CUMNMF) is presented on GPU computing. From the experimental results, CUMNMF achieves a 20X speedup compared with MNMF, as well as a lower time complexity and space requirement.
Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images

Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera.In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer.We proposed novel features that are suitable for depth data.These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements.Lastly, advanced hidden Markov model is used to recognize different activities.In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition.Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate.Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games.
An Analysis and Implementation of the FFDNet Image Denoising Method

FFDNet is a recent image denoising method based on a convolutional neural network architecture.In contrast to other existing neural network denoisers, FFDNet exhibits several desirable properties such as faster execution time and smaller memory footprint, and the ability to handle a wide range of noise levels with a single network model.The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications.In this paper we propose an open-source implementation of the method based on PyTorch, a popular machine learning library for Python.Code for the training of the network is also provided.We also discuss the characteristics of the architecture of this algorithm and we compare it to other similar methods.
Learning Deep Conditional Neural Network for Image Segmentation

Combining Convolutional Neural Networks (CNNs) with Conditional Random Fields (CRFs) achieves great success among recent object segmentation methods. There are two advantages by such usage. First, CNNs can extract low-level features, which are very similar to the extracted features in primates' primary visual cortex (V1). Second, CRFs can set up the relationship between input features and output labels in a direct way. In this paper, we extend the first advantage by using CNNs for low-level feature extraction and a Structured Random Forest (SRF)-based border ownership detector for high-level feature extraction, which are similar to the outputs of primates secondary visual cortex (V2). Compared to the CRF model, an improved Conditional Boltzmann Machine (CBM), which has a multi-channel visible layer, is proposed to model the relationship between predicted labels, local and global contexts of objects with multi-scale and multilevel features. Besides, our proposed CBM model is extended for object parsing by using multivisible branches instead of a single visible layer of CBM, which cannot only segment the whole body but also the parts of the body under. These visible branches use each branch for the segmentation of the whole body or one of the body parts. All branches share the same hidden layers of CBM and train the branches under an iterative way. By exploiting object parsing, the whole body segmentation performance of object is improved. To refine the segmentation output, two kinds of optimization algorithms are proposed. The superpixel-based algorithm can re-label the overlapped regions of multiple kinds of objects. The other curve correction algorithm corrects the edges of segmented object parts by using smooth edges under a curve similarity criterion. Experiments demonstrate that our models yield competitive results for object segmentation on the PASCAL VOC 2012 dataset and for object parsing on the PennFudan Pedestrian Parsing dataset, Pedestrian Parsing Surveillance Scenes dataset, Horse-Cow parsing dataset, and PASCAL Quadrupeds dataset.