Reviewerly/exHarmony huggingface dataset:
Key Aspects of Cyberlearning Resources with Compelling Results

Abstract Key Aspects of Cyberlearning Resources with Compelling ResultsThanks to resources like Scratch, PhET, and the Mobile Studio, cyberlearning is facilitating thedevelopment of 21st century skills. With these tools, learners are creating and sharing interactivemedia, manipulating computer simulations to understand physics in the world around them, andtweeting about the fascinating outputs generated by their personalized circuit boards. This is justa subset of the infinite possibilities cyberlearning affords, and Program Officers (PO) in theDivision of Undergraduate Education (DUE) at the National Science Foundation (NSF) areinterested in exploring more. Such interest was the impetus for this study.NSF defines cyberlearning as “the use of networked computing and communicationstechnologies to support learning (NSF Taskforce on Cyberlearning, 2008, p.5). The positiveoutcomes of existing cyberlearning resources hint at the promise cyberlearning holds fromsatisfying DUE’s mission to promote excellence in undergraduate science, technology,engineering, and mathematics (STEM) education. As part of moving forward, however, there is aneed to understand elements of the existing resources that have already achieved positiveoutcomes. Thus, the purpose of this study is to identify and highlight key elements of existingcyberlearning resources with compelling results.A sequential explanatory mixed methods research design (Creswell, 2011) was used to addressthis topic. Initially, a population of approximately 100 cyberlearning awards was generated fromamong over 1,000 NSF-funded projects POs have highlighted in the NSF Highlights over thepast 10 years. Selection criteria were used to identify cyberlearning awards with compellingresults to serve as exemplars. One-hour interviews were conducted with developers of the 15cyberlearning resources selected to garner insights on their approached to development,implementation, and dissemination. Interview responses were analyzed by coding, and identifythemes. Insights about their keys to the positive outcomes they have achieved will discussed inthis paper.Creswell, J.W., Plano Clark, V. (2011). Designing and Conducting Mixed Methods Research (2nded.). Thousand Oaks, CA: Sage Publications.NSF Taskforce on Cyberlearning (2008). Fostering Learning in a Networked World: TheCyberlearning Opportunities and Challenges. Retrieved from:http://www.nsf.gov/pubs/2008/nsf082004/nsf08204_1.pdf
ANALISIS DAN DESAIN SISTEM INFORMASI  AKUNTANSI ASET TETAP PADA  HOTEL G SUITES SURABAYA

This research is a case study of designing and analyzing about accounting information systems of fixed asset. Currently, the tourism sector as an economic activity has become a priority for the development of a number of countries. The tourism sector in Indonesia is increasingly growing, and it is spurring growth of hotel in Indonesia. Therefore, this research is about improve the accounting information systems of fixed asset in a hotel. The object of this research is Hotel G Suites Surabaya. The scope of this research is limited to the accounting information system of fixed asset that consists of acquisition cycle, maintenance cycle, and the termination cycle of fixed assets. The methods of this research are observation, interview, and documentation. The hotel has prob-lems such as loss of goods in the hotel rooms and restaurant. The hotel doesn’t have maintenance procedure and termination pro-cedure of fixed assets, even though the procedure is very important for the hotel to reduce the risk of loss of fixed assets due to errors procedure. Hotel has implemented internal control. Internal control of the hotel still has deficiency. Hotel G Suites Surabaya needs to fix the job description, organizational structure, the design of documents, use the asset number for fixed assets and the serial number for document, and also fix the purchase procedure of fixed.
Sequence Data Mining for Adverse Event Prediction and Action Recommendation

Many real-life data mining applications use sequence data modeling in which data is represented as a sequence.A temporal sequence is finite ordered list of events (t 1 ,e 1 ), (t 2 ,e 2 ), …,(t n ,e n ) where t i represents time and e i represents the event taking place at time t i .e i takes place before e i+1 for 1≤ i ≤ n-1.This model can be used in data mining, called sequence data mining, to predict certain event that may take place at a specific time.Sequence data mining has a wide range of applications.This data mining technique can be used for prediction of adverse events and recommend proper actions to be taken as needed.In aviation safety, the future of a flight can be predicted as a sequence and proper action can be recommended to avoid dangerous situations that a flight may get into otherwise.In health care system, the future of a bacterial infection can be predicted and proper medicine can be prescribed for different situations to bring the patient's illness to an end.In the marketing, customer shopping can be monitored and certain action can be taken, such as mailing coupons, to encourage the customer for further shopping of relevant products.In the real-life situations such as manufacturing, sensors' data can be analyzed to control operations and predict dangerous situations and recommend proper actions to be taken.This paper discusses sequence representation, implementation, and its application for a number of different cases.
Gender Effects in Directed versus Incidental Learning in a 3D Virtual World Simulation

Virtual worlds have the potential to enable and enhance online learning outcomes.Because learning in threedimensional (3D) designed learning spaces depends on learners' spatial processing abilities, we need to understand how these abilities may affect online learning outcomes.Building on the hunter-gatherer theory of gender difference in spatial abilities, we examined how gender interacts with learning type (directed vs. incidental) to affect learning in virtual world (VR) simulations of objects.Specifically, we theorized that men's and women's spatial abilities would lead to differential outcomes based on the type of learning that the instructor designed.Using a between-subjects 2 x 2 factorial design (directed vs. incidental learning and male vs. female), we found that incidental learning benefitted women and that directed learning benefitted men.Our findings counter the traditional view that males outperform females in learning tasks that engage spatial abilities in a virtual world.We urge educators to consider such gender effects on learning when employing VR simulations of objects.
Game-inspired Pedagogical Conversational Agents: A Systematic Literature Review

Pedagogical conversational agents (PCAs) are an innovative way to help learners improve their academic performance via intelligent dialog systems.However, PCAs have not yet reached their full potential.They often fail because users perceive conversations with them as not engaging.Enriching them with game-based approaches could contribute to mitigating this issue.One could enrich a PCA with game-based approaches by gamifying it to foster positive effects, such as fun and motivation, or by integrating it into a game-based learning (GBL) environment to promote effects such as social presence and enable individual learning support.We summarize PCAs that are combined with game-based approaches under the novel term "game-inspired PCAs".We conducted a systematic literature review on this topic, as previous literature reviews on PCAs either have not combined the topics of PCAs and GBL or have done so to a limited extent only.We analyzed the literature regarding the existing design knowledge base, the game elements used, the thematic areas and target groups, the PCA roles and types, the extent of artificial intelligence (AI) usage, and opportunities for adaptation.We reduced the initial 3,034 records to 50 fully coded papers, from which we derived a morphological box and revealed current research streams and future research recommendations.Overall, our results show that the topic offers promising application potential but that scholars and practitioners have not yet considered it holistically.For instance, we found that researchers have rarely provided prescriptive design knowledge, have not sufficiently combined game elements, and have seldom used AI algorithms as well as intelligent possibilities of user adaptation in PCA development.Furthermore, researchers have scarcely considered certain target groups, thematic areas, and PCA roles.Consequently, our paper contributes to research and practice by addressing research gaps and structuring the existing knowledge base.
Estimation of gravitational acceleration with quantum optical interferometers

The precise estimation of the gravitational acceleration is important for various disciplines. We consider making such an estimation using quantum optics. A Mach-Zehnder interferometer in an ``optical fountain'' type arrangement is considered and used to define a standard quantum limit for estimating the gravitational acceleration. We use an approach based on quantum field theory on a curved, Schwarzschild metric background to calculate the coupling between the gravitational field and the optical signal. The analysis is extended to include the injection of a squeezed vacuum to the Mach-Zehnder arrangement and also to consider an active, two-mode SU(1,1) interferometer in a similar arrangement. When detection loss is larger than $8%$, the SU(1,1) interferometer shows an advantage over the MZ interferometer with single-mode squeezing input. The proposed system is based on current technology and could be used to examine the intersection of quantum theory and general relativity as well as for possible applications.
A Step-by-Step Guide to Using BioNetFit

BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with deterministic and stochastic simulators that accept BioNetGen language (BNGL)-formatted files as inputs, such as those available within the BioNetGen framework. BioNetFit can be used on a laptop or stand-alone multicore workstation as well as on many Linux clusters, such as those that use the Slurm Workload Manager to schedule jobs. BioNetFit implements a metaheuristic population-based global optimization procedure, an evolutionary algorithm (EA), to minimize a user-defined objective function, such as a residual sum of squares (RSS) function. BioNetFit also implements a bootstrapping procedure for determining confidence intervals for parameter estimates. Here, we provide step-by-step instructions for using BioNetFit to estimate the values of parameters of a BNGL-encoded model and to define bootstrap confidence intervals. The process entails the use of several plain-text files, which are processed by BioNetFit and BioNetGen. In general, these files include (1) one or more EXP files, which each contains (experimental) data to be used in parameter identification/bootstrapping; (2) a BNGL file containing a model section, which defines a (rule-based) model, and an actions section, which defines simulation protocols that generate GDAT and/or SCAN files with model predictions corresponding to the data in the EXP file(s); and (3) a CONF file that configures the fitting/bootstrapping job and that defines algorithmic parameter settings.
Coherent confluence modulo relations and double groupoids

A coherent presentation of an n-category is a presentation by generators, relations and relations among relations. Confluent and terminating rewriting systems generate coherent presentations, whose relations among relations are defined by confluence diagrams of critical branchings. This article introduces a procedure to compute coherent presentations when the rewrite relations are defined modulo a set of axioms. Our coherence results are formulated using the structure of n-categories enriched in double groupoids, whose horizontal cells represent rewriting paths, vertical cells represent the congruence generated by the axioms and square cells represent coherence cells induced by diagrams of confluence modulo. We illustrate our constructions on rewriting systems modulo commutation relations in commutative monoids, isotopy relations in pivotal monoidal categories, and inverse relations in groups.
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics (with Discussion)

Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks. However, due to the flexibility of these models, the consequences of prior choices can be opaque. And so prior specification can be relatively difficult. At the same time, prior choice can have a substantial effect on posterior inferences. Thus, considerations of robustness need to go hand in hand with nonparametric modeling. In the current paper, we tackle this challenge by exploiting the fact that variational Bayesian methods, in addition to having computational advantages in fitting complex nonparametric models, also yield sensitivities with respect to parametric and nonparametric aspects of Bayesian models. In particular, we demonstrate how to assess the sensitivity of conclusions to the choice of concentration parameter and stick-breaking distribution for inferences under Dirichlet process mixtures and related mixture models. We provide both theoretical and empirical support for our variational approach to Bayesian sensitivity analysis.
How to Watermark Cryptographic Functions by Bilinear Maps

We introduce a notion of watermarking for cryptographic functions and propose a concrete scheme for watermarking cryptographic functions. Informally speaking, a digital watermarking scheme for cryptographic functions embeds information, called a mark, into functions such as one-way functions and decryption functions of public-key encryption. There are two basic requirements for watermarking schemes. A mark-embedded function must be functionally equivalent to the original function. It must be difficult for adversaries to remove the embedded mark without damaging the original functionality. In spite of its importance and usefulness, there have only been a few theoretical works on watermarking for functions (or programs). Furthermore, we do not have rigorous definitions of watermarking for cryptographic functions and concrete constructions. To solve the problem above, we introduce a notion of watermarking for cryptographic functions and define its security. Furthermore, we present a lossy trapdoor function (LTF) based on the decisional bilinear Diffie-Hellman problem problem and a watermarking scheme for the LTF. Our watermarking scheme is secure under the symmetric external Diffie-Hellman assumption in the standard model. We use techniques of dual system encryption and dual pairing vector spaces (DPVS) to construct our watermarking scheme. This is a new application of DPVS.
Decentralized Poisson Multi-Bernoulli Filtering for Extended Target Tracking.

A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization of single hypothesis tracks, and fusion of vehicle information based on a fusion mapping. Numerical results demonstrate the performance.
Bayesian adaptive Lasso for quantile regression models with nonignorably missing response data

Handling data with the nonignorably missing mechanism is still a challenging problem in statistics. In this paper, we develop a fully Bayesian adaptive Lasso approach for quantile regression models with nonignorably missing response data, where the nonignorable missingness mechanism is specified by a logistic regression model. The proposed method extends the Bayesian Lasso by allowing different penalization parameters for different regression coefficients. Furthermore, a hybrid algorithm that combined the Gibbs sampler and Metropolis-Hastings algorithm is implemented to simulate the parameters from posterior distributions, mainly including regression coefficients, shrinkage coefficients, parameters in the non-ignorable missing models. Finally, some simulation studies and a real example are used to illustrate the proposed methodology.
Hierarchical Representations with Poincar\'e Variational Auto-Encoders

The Variational Auto-Encoder (VAE) model has become widely popular as a way to learn at once a generative model and embeddings for observations living in a high-dimensional space. In the real world, many such observations may be assumed to be hierarchically structured, such as living organisms data which are related through the evolutionary tree. Also, it has been theoretically and empirically shown that data with hierarchical structure can efficiently be embedded in hyperbolic spaces. We therefore endow the VAE with a hyperbolic geometry and empirically show that it can better generalise to unseen data than its Euclidean counterpart, and can qualitatively recover the hierarchical structure.
Multi-Agent Pathfinding (MAPF) with Continuous Time.

MAPF is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF were on grid, assumed all actions cost the same, agents do not have a volume, and considered discrete time steps. In this work we propose a MAPF algorithm that do not assume any of these assumptions, is complete, and provides provably optimal solutions. This algorithm is based on a novel combination of SIPP, a continuous time single agent planning algorithms, and CBS, a state of the art multi-agent pathfinding algorithm. We analyze this algorithm, discuss its pros and cons, and evaluate it experimentally on several standard benchmarks.
Calibration with Bias-Corrected Temperature Scaling Improves Domain Adaptation Under Label Shift in Modern Neural Networks.

Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. Label shift arises in settings like medical diagnosis, where a classifier trained to predict disease given symptoms must be adapted to scenarios where the baseline prevalence of the disease is different. Given estimates of p(y|x) from a predictive model, Saerens et al. proposed an efficient maximum likelihood algorithm to correct for label shift that does not require model retraining, but a limiting assumption of this algorithm is that p(y|x) is calibrated, which is not true of modern neural networks. Recently, Black Box Shift Learning (BBSL) and Regularized Learning under Label Shifts (RLLS) have emerged as state-of-the-art techniques to cope with label shift when a classifier does not output calibrated probabilities, but both methods require model retraining with importance weights and neither has been benchmarked against maximum likelihood. Here we (1) show that combining maximum likelihood with a type of calibration we call bias-corrected calibration outperforms both BBSL and RLLS across diverse datasets and distribution shifts, (2) prove that the maximum likelihood objective is concave, and (3) introduce a principled strategy for estimating source-domain priors that improves robustness to poor calibration. This work demonstrates that the maximum likelihood with appropriate calibration is a formidable and efficient baseline for label shift adaptation; notebooks reproducing experiments available at this https URL
Indoor Location and Collision Feedback for a Powered Wheelchair System Using Machine Learning

In 2015 a powered wheelchair system to detect and avoid objects was enhanced with a Raspberry Pi to extend the sensory input sources to the system in order to record information about its environment. Wheelchair users are not always able to use simple controls such as joysticks to drive and they may have to control the wheelchair using their tongue, head or feet. This can increase the effort it takes to learn how to drive and therefore it becomes important to track and observe how a wheelchair user is progressing. The research described in this paper employs machine learning to use wireless access points and predict its location, and with prolonged use will learn routes between rooms and buildings. The system uses location and accelerometer data to present information about driving patterns and collisions behaviour. The primary user interface is for the wheelchair user to orientate themselves indoors whilst driving and a secondary user interface is displaying past information to a carer to inform about incidents and general tracking of the wheelchair user.
Video Foreground Detection Algorithm Based on Fast Principal Component Pursuit and Motion Saliency

Aiming at the shortcoming of being unsuitable for dynamic background and high computational complexity of the existing RPCA- (robust principal component analysis-) based block-sparse moving object detection method, this paper proposes a two-stage foreground detection framework based on motion saliency for video sequence. At the first stage, the observed image sequence is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA method via fast PCP (principal component pursuit). At the second stage, the sparse foreground blocks are obtained according to the spectral residuals and the spatial correlation of the foreground region. Finally, the block-sparse RPCA algorithm through fast PCP is used to estimate foreground areas dynamically and to reconstruct the foreground objects. Extensive experiments demonstrate that our method can exclude the interference of background motion and change, simultaneously improving the detection rate of small targets.
The Use of Copyrighted Works by AI Systems: Art Works in the Data Mill

We shall first introduce the use of artificial intelligence (AI) in producing new intellectual creations, distinguishing approaches based on knowledge representation and on machine learning. Then we shall provide an overview of some significant applications of AI to the production of intellectual creations, distinguishing the extent to which they depend on pre-existing works, and the different ways in which such pre-existing works are used in the creative process. In addition, we shall discuss some methods to automatically assess the similarity of works and styles, in the context of AI technologies for text generation. Finally, we shall discuss the legal aspects of AI-reuse of copyrighted works, focusing on the rights of the authors of such works relative to the process and the outputs of AI.
Uncloneable Quantum Encryption via Random Oracles

Quantum information is well-known to achieve cryptographic feats that are unattainable using classical information alone. Here, we add to this repertoire by introducing a new cryptographic functionality called uncloneable encryption. This functionality allows the encryption of a classical message such that two collaborating but isolated adversaries are prevented from simultaneously recovering the message, even when the encryption key is revealed. Clearly, such functionality is unattainable using classical information alone. We formally define uncloneable encryption, and show how to achieve it using Wiesner's conjugate coding, combined with a quantum-secure pseudorandom function (qPRF). Modelling the qPRF as a quantum random oracle, we show security by adapting techniques from the quantum one-way-to-hiding lemma, as well as using bounds from quantum monogamy-of-entanglement games.