Reviewerly/exHarmony huggingface dataset:
Balancing Goal Obfuscation and Goal Legibility in Settings with Cooperative and Adversarial Observers

In order to be useful in the real world, AI agents need to plan and act in the presence of others, who may include adversarial and cooperative entities. In this paper, we consider the problem where an autonomous agent needs to act in a manner that clarifies its objectives to cooperative entities while preventing adversarial entities from inferring those objectives. We show that this problem is solvable when cooperative entities and adversarial entities use different types of sensors and/or prior knowledge. We develop two new solution approaches for computing such plans. One approach provides an optimal solution to the problem by using an IP solver to provide maximum obfuscation for adversarial entities while providing maximum legibility for cooperative entities in the environment, whereas the other approach provides a satisficing solution using heuristic-guided forward search to achieve preset levels of obfuscation and legibility for adversarial and cooperative entities respectively. We show the feasibility and utility of our algorithms through extensive empirical evaluation on problems derived from planning benchmarks.
Investigating the Banking Sector Development Transmission Mechanism of Financial Development to Growth: Evidence from Sub-Saharan Africa (SSA)

The search for financial developmentâ€™s transmission channel to growth has always been updated in the literature. While there has not been a consensus on this matter, empirical findings on finance-growth nexus have been ambiguous. Relying on this, we investigate its bank development transmission channel to growth in a panel of twenty-eight Sub-Saharan Africa (SSA) countries from 2000-2016. Having adopted the augmented Solow (1956) and Mankiw et al. (1992) growth model, the fixed effect and dynamic system GMM estimation techniques reveals a negative non-significant and positive significant direct impact of finance on growth in the static and dynamic models respectively, thereby suggesting institutional (dynamic) factors that can spur finance. Secondly, the non-linear effects of bank development had a direct positive significant impact on growth and its marginal-effects before and after the financial crisis of 2007/08 were relatively stable. This implies that banks in SSA were relatively stable in financial intermediation; therefore SSA countries need to reinforce and improve its banking policy through FinTechs adoption. Finally, the interaction between bank development and financial development significantly increase steady-state growth. This implies that SSA economies can promote steady-state growth from financial development only when a threshold of bank development is reached.
An App Manifest and Installable Angular App

Up until this point in the book, we have focused on the core feature of Progressive Web App (PWA), Service Worker. It enables us to cache our static assets as well as dynamic content. The app will continue to work offline, which is especially important on mobile devices. However, an app’s “look and feel” is another important factor that enhances the user experience to truly delight users.
Quantification of White Matter Lesions on Brain MRI with 2D Fuzzy Weighted Recurrence Networks

Removed.
Data Cooperatives: Towards a Foundation for Decentralized Personal Data Management.

Data cooperatives with fiduciary obligations to members provide a promising direction for the empowerment of individuals through their own personal data. A data cooperative can manage, curate and protect access to the personal data of citizen members. Furthermore, the data cooperative can run internal analytics in order to obtain insights regarding the well-being of its members. Armed with these insights, the data cooperative would be in a good position to negotiate better services and discounts for its members. Credit Unions and similar institutions can provide a suitable realization of data cooperatives.
The Ethics of Exploring Gender Issues in a Time of #Metoo

Earlier this fall, I had the opportunity to attend the Eighth Annual Symposium on Digital Ethics at the School of Communication at Loyola University Chicago. One of the invited speakers was Susan F...
The Analysis on Career Selection and Decision Making of Graduates of Teachers College

본 연구는 4년제 사범계열 대학 졸업생의 진로선택 및 결정과 진로진출 방향에 대한 탐색하였고, 개인의 주관성이 반영된 잠재변인인 대학교육만족도(교육과정 만족, 교육인프라 만족), 직무만족도(외 재적, 내재적), 직업가치관(내적, 외적), 삶의 만족도를 중심으로 두 집단(비교사, 교사) 간 차이를 GOMS(대졸자직업이동경로조사) 데이터를 활용하여 분석하였다. 분석결과, 대학교육만족도(교육과정 만족, 교육인프라 만족), 직무만족도(외재적, 내재적), 직업가치관(내적, 외적), 삶의 만족도 모두 교사집단의 잠재평균값이 높게 도출되었다. 이 중에서도 두 집단의 평균차이가 크게 나타난 변인은 ‘복리후생제도’, ‘고용의 안정성’, ‘일자리에 대한 사회적 평판’이었다. 이러한 연구결과를 중심으로 사범계열 대학생들의 진로지도 및 진로교육 프로그램의 강화를 위해 진로경로를 구분하여 진로 로드 맵을 마련할 것과 학생역량통합시스템이나 졸업생통합관리와 같은 빅데이터를 적극적으로 활용하여각 학과만의 진로정보를 체계적으로 관리해 나갈 것을 제언하였다.In this study, career selection, career decision making, career advancement direction of the graduates of teachers college were explored. The latent mean difference between the two groups (non-teachers, teachers) was analyzed, focusing on the satisfaction level of college education (curricular satisfaction, educational infrastructure satisfaction), job satisfaction (external and intrinsic), job value (internal and external), and life satisfaction. The analysis was conducted using GOMS data to conduct T-Test and Latent Mean Analysis. The results of the analysis showed that the potential averages of the teachers’ groups were high in both college education satisfaction, job satisfaction, and job value, and life satisfaction. Among them, the variables that showed the average difference between the two groups were welfare system , employment stability , and social reputation for jobs . Based on these results, we suggested that we should set up a career road map by classifying career paths and strengthening career guidance and career education programs for college students in the field of education, and actively utilize big data such as student competency integration system and integrated management of graduates to systematically manage career information of each department.
Pengaruh Model Pembelajaran SAVI (Somantik, Auditori, Visual Dan Intelektual) Terhadap Kemampuan Menulis Karangan Argumentasi Siswa SMA

This research is motivated by the low writing skill of students' argumentative essay causing low student learning outcomes. The method used in this study is the experimental method conducted at SMAN 17 Pandeglang by taking class X students in the 2015/2016 academic year as a population. The sample technique used is cluster random sampling. The draw results conducted on the five existing classes were obtained, namely the X-IPS 1 class as the experimental class and X-IPS 3 class as the control class. Each class is 25 and 24 students. Thus the number of samples in this study were 49 students. The experimental class is given treatment, namely the learning process using the SAVI model (Somantik, Auditory, Visual and Intellectual). Whereas the control class uses the expository model. Based on the results of data analysis in statistical calculations, it can be concluded that there is a positive effect of SAVI learning model at student writing skill of  argumentative essay at class X, SMAN 17 Pandeglang 2015/2016 academic year. This is indicated by data from the analysis using the t-test formula, obtained t-count 2.984. Whereas t-table for the significance level of 0.05 with degrees of freedom (df) 47 obtained t-table 2.014. Because t-count is greater than t-table (t-count> t-table), which is 2.984> 2.014, then the null hypothesis (Ho) is rejected. By rejecting of the null hypothesis (Ho), then the alternative hypothesis (Ha) is accepted. Thus, it means that there is significant effect of using SAVI learning model at student writing skill of argumentative essay.
Anticipatory Bayesian Policy Selection for Online Adaptation of Collaborative Robots to Unknown Human Types

As a key component of collaborative robots (cobots) working with humans, existing decision-making approaches try to model the uncertainty in human behaviors as latent variables. However, as more possible contingencies are covered by such intention-aware models, they face slow convergence times and less accurate responses. For this purpose, we present a novel anticipatory policy selection mechanism built on existing intention-aware models, where a robot is required to choose from an existing set of policies based on an estimate of the human. Each of these intention-aware robot models anticipates and adapts to a different human's short-term changing behaviors. Our contribution is the Anticipatory Bayesian Policy Selection (ABPS) mechanism which selects from a library of different response policies that are generated from such models, and converges to a reliable policy after as few interactions as possible when faced with unknown humans. The selection is based on the estimation of the human in terms of long-term workplace characteristics that we call types, such as level of expertise, stamina, attention and collaborativeness. Our results show that incorporating this policy selection mechanism contributes positively to the efficiency and naturalness of the collaboration, when compared to the best intention-aware model in hindsight running alone.
General-Sum Cyber Deception Games under Partial Attacker Valuation Information

The rapid increase in cybercrime, causing a reported annual economic loss of $600 billion [20], has prompted a critical need for effective cyber defense. Strategic criminals conduct network reconnaissance prior to executing attacks to avoid detection and establish situational awareness via scanning and fingerprinting tools. Cyber deception attempts to foil these reconnaissance efforts; by disguising network and system attributes, among several other techniques. Cyber Deception Games (CDG) is a game-theoretic model for optimizing strategic deception, and can apply to various deception methods. Recently introduced initial model for CDGs assumes zero-sum payoffs, implying directly conflicting attacker motives, and perfect defender knowledge on attacker preferences. These unrealistic assumptions are fundamental limitations of the initial zero-sum model, which we address by proposing a general-sum model that can also handle uncertainty in the defender's knowledge.
Shrinking

Arseny Tolmachev, Daisuke Kawahara, Sadao Kurohashi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Target Information Fusion Based on Memory Network for Aspect-Level Sentiment Classification

Aspect-level sentiment classification is a fine-grained task that provides more complete and deeper analysis results. Attention networks are widely used for aspect-level sentiment classification task. However, when multiple target words in a sentence contain opposite sentiments or the expressions of different targets are similar, the network tends to perform poorly. Our studies find that the method of averaging a sentence or target words weakens the capacity of key words. A target information fusion memory network is proposed to solve this problem in this paper. Firstly, the feature of sentences is extracted through a Bi-LSTM network. Then, the feature of target is extracted incorporated into the sentence feature extracted. Then, the memory information of the specific target is formed by the position coding. Finally, the recurrent attention network is utilized to extract the sentiment expression from the memory. Compare with IAN, the method proposed achieves 1.5% and 1.9% accuracy improvement on SemEvil2014 restaurant dataset and self-defined Chinese mobile phone dataset, respectively. A further extend experiment proves that the proposed method can effectively improve the performance in the case of complex sentences.
Preference Learning in Automated Negotiation Using Gaussian Uncertainty Models

In this paper, we propose a general two-objective Markov Decision Process (MDP) modeling paradigm for automated negotiation with incomplete information, in which preference elicitation alternates with negotiation actions, with the objective to optimize negotiation outcomes. The key ingredient in our MDP framework is a stochastic utility model governed by a Gaussian law, formalizing the agent's belief (uncertainty) over the user's preferences. Our belief model is fairly general and can be updated in real time as new data becomes available, which makes it a fundamental modeling tool.
How to make a machine learn continuously: a tutorial of the Bayesian approach

How to build a machine that can continuously learn from observations in its life and make accurate inference/prediction? This is one of the central questions in Artificial Intelligence. Many challenges are present, such as the difficulty of learning from infinitely many observations (data), the dynamic nature of the environments, noisy and sparse data, the intractability of posterior inference, etc. This tutorial will discuss how the Bayesian approach provides a natural and efficient answer. We will start from the basic of Bayesian models, and then the variational Bayes method for inference. Next, we will discuss how to learn a Bayesian model from an infinite sequence of data. Some challenges such as catastrophic forgetting phenomenon, concept drifts, and overfitting will be discussed.
The analysis of tuberculosis funds efficiency on high burden countries

Tuberculosis remains a major global health problem and ranks as the second leading cause of death from infectious disease worldwide. Countries devote their budgets to overcome the tuberculosis problem. An efficient use of these budgets will arguably reduce the number of tuberculosis cases and eventually give a positive impact to the economy. This paper aims it aims to estimate the technical efficiency scores of tuberculosis funds on high-burden countries by using Data Envelopment Analysis (DEA) method. Further, this study analyzes other environmental factors that are crucial to increase the efficiency scores by using Tobit method. DEA shows that some countries exhibit high efficiency scores while others exhibit low efficiency scores. It also informs how countries use funds to maximize their results. Meanwhile, the Tobit estimation shows that taxing cigarettes and committing budgets to control tobaccos have positive marginal effects on technical efficiency scores.
Sensitivity Analysis of the Neural Networks Randomized Learning

Randomized algorithms for learning feedforward neural networks are increasingly used in practice. They offer very speed training because the only parameters that are learned are the output weights. Parameters of hidden neurons are generated randomly once and need not to be adjusted. The key issue in randomized learning algorithms is to generate parameters in a right way to ensure good approximation and generalization properties of the network. Recently the method of generating hidden nodes parameters was proposed [1], which ensures better adjustment of the random parameters to the target function and better distribution of neurons in the input space, when comparing to the previous approaches. In this work the new method is tested in terms of sensitivity to the number of neurons, noise in data and data deficit. Experiments shows better results for the new method in comparison to the existing approach of generating random parameters of the network.
Compact Representations of Dynamic Video Background Using Motion Sprites

We present a method to extend the idea of sprite coding to videos containing a wide variety of naturally occurring background motion, which could potentially be incorporated into existing and future video standards. The existing MPEG-4 part 2 standard, now almost 20 years old, provides the ability to store objects in separate layers, and includes a sprite mode where the background layer is generated by cropping a still image based on frame-wide global motion parameters, but videos containing more general background motion cannot be effectively encoded with sprite mode. We propose a perceptually motivated lossy compression algorithm, where oscillatory background motion can be compactly encoded. Our model achieves a low bit rate by referencing a time-invariant representation of the optical flow with only a few added parameters per frame. At very low bit rates, our technique can provide dynamic backgrounds at a visual quality that may not be achievable by traditional methods which are known to produce unacceptable blocking and ringing artifacts.
Text Language Identification Using Attention-Based Recurrent Neural Networks

The main purpose of this work is to explore the use of Attention-based Recurrent Neural Networks for text language identification. The most common, statistical language identification approaches are effective but need a long text to perform well. To address this problem, we propose the neural model based on the Long Short-Term Memory Neural Network augmented with the Attention Mechanism. The evaluation of the proposed method incorporates tests on texts written in disparate styles and tests on the Twitter posts corpus which comprises short and noisy texts. As a baseline, we apply a widely used statistical method based on a frequency of occurrences of n-grams. Additionally, we investigate the impact of an Attention Mechanism in the proposed method by comparing the results with the outcome of the model without an Attention Mechanism. As a result, the proposed model outperforms the baseline and achieves 97,98% accuracy on the test corpus covering 36 languages and keeps the accuracy also for the Twitter corpus achieving 91,6% accuracy.
Embedding Meta Information into Visualizations

In this work, we study how to co-locate meta information with visualizations by directly embedding information into visualizations. This allows for visualizations to carry provenance and authorship information themselves for reproducibility. We call these self-describing visualizations-reproducible, authenticatable, and documentable. Self-describing visualizations can be used to extend existing visualization provenance systems. Herein, we start with a survey of existing digital image watermarking literature. We search for and classify watermarking algorithms that can support scientific visualizations. Using our payload-resilience testing framework, we evaluate and recommend algorithms supporting various use cases in the payload-resiliency space, and present guidelines for optimizing visualizations to improve payload capacities and embedding robustness. We demonstrate the efficacy of self-describing visualizations with two sample application implementations: (1) adding an embedding filter as a part the standard rendering pipeline, (2) creating a web reader to automatically and reliably extract provenance information from scientific publications for review and dissemination.
Shareflow: A Visualization Tool for Information Diffusion in Social Media

The popularity of social network has rapidly increased for many years. Social network continues to provide great functions to its user like sharing or diffusing the information on the social network. There are some interesting issues to the journalists are embedded inside the information diffusion. Therefore, we are encouraged to propose a visualization tool, “Shareflow”, which can be used to observe information sharing activities in the social network and to explore opinion leaders and the propagation path caused by the information diffusion. Our approach consists of two parts. The first part focuses on propagation path visualization for a single post in social media. The hierarchical edge bundles method is adopted to optimize the layout and reduce visual clutter caused by excessive information. The second part focuses on the visualization for a summary of posts, which provides a tool for analyzing active users through their sharing and comments activities. A real-time interactive interface that demonstrates concurrently the breadth and depth of information diffusion is also provided.
Person Re-identification through Clustering and Partial Label Smoothing Regularization

In this paper, we propose a new label smoothing regularization scheme for person re-identification. We first use an unsupervised method for discriminative learning representation. We apply a clustering algorithm on the learned feature to partition the training set into k groups of equal variance and derive a shared space for similar images. Secondly, a GAN model is fed with each cluster to produce samples with relatively similar features to the original space. Our method consists of assigning an adaptive smooth label distribution to each generated sample according to their original cluster. To train our model, we define a new objective function which takes into account the generated samples and fine-tuned a CNN baseline using the objective function. Our model learns to exploit the samples generated by the GAN model to boost the performance of the person re-id by improving generalization. Extensive evaluations were conducted on four large-scale datasets to validate the advantage of the proposed model.
Evaluation of Omnipresent Virtual Agents Embedded as Temporarily Required Assistants in Immersive Environments

When designing the behavior of embodied, computer-controlled, human-like virtual agents (VA) serving as temporarily required assistants in virtual reality applications, two linked factors have to be considered: the time the VA is visible in the scene, defined as presence time (PT), and the time till the VA is actually available for support on a user's calling, defined as approaching time (AT). Complementing a previous research on behaviors with a low VA's PT, we present the results of a controlled within-subjects study investigating behaviors by which the VA is always visible, i.e., behaviors with a high PT. The two behaviors affecting the AT tested are: following, a design in which the VA is omnipresent and constantly follows the users, and busy, a design in which the VA is self-reliantly spending time nearby the users and approaches them only if explicitly asked for. The results indicate that subjects prefer thefollowing VA, a behavior which also leads to slightly lower execution times compared to busy.
Compact TLS 1.3

This document specifies a compact version of TLS 1.3. It isisomorphic to TLS 1.3 but saves space by trimming obsolete material,tighter encoding, and a template-based specialization technique. cTLSis not directly interoperable with TLS 1.3, but it should eventuallybe possible for a cTLS/TLS 1.3 server to exist and successfullyinteroperate.