nils-herrmann/clean_scirepeval huggingface dataset:
A Survey on Trust Modeling from a Bayesian Perspective

In this paper, we are concerned with trust modeling for agents in networked computing systems. As trust is a subjective notion that is invisible, implicit and uncertain in nature, many attempts have been made to model trust with aid of Bayesian probability theory, while the field lacks a global comprehensive analysis for variants of Bayesian trust models. We present a study to fill in this gap by giving a comprehensive review of the literature. A generic Bayesian trust (GBT) modeling perspective is highlighted here. It is shown that all models under survey can cast into a GBT based computing paradigm as special cases. We discuss both capabilities and limitations of the GBT perspective and point out open questions to answer for advancing it to become a pragmatic infrastructure for analyzing intrinsic relationships between variants of trust models and for developing novel ones for trust evaluation.
Origins of Serious Games

The purpose of this chapter is to discuss the historical origins of Serious Games to try to understand where the current wave of “Serious Games” comes from. We first review the origins of the “Serious Games” oxymoron. We will then analyse digital games designed for serious purposes before the 2000’s. Such games can be traced back to the beginning of the history of video games. We will use all these elements to discuss how the current wave of “Serious Games” began; and to highlight the differences between “Serious Games” and their ancestors.
Deep Generative Stochastic Networks Trainable by Backprop

We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based on learning the transition operator of a Markov chain whose stationary distribution estimates the data distribution. The transition distribution of the Markov chain is conditional on the previous state, generally involving a small move, so this conditional distribution has fewer dominant modes, being unimodal in the limit of small moves. Thus, it is easier to learn because it is easier to approximate its partition function, more like learning to perform supervised function approximation, with gradients that can be obtained by backprop. We provide theorems that generalize recent work on the probabilistic interpretation of denoising autoencoders and obtain along the way an interesting justification for dependency networks and generalized pseudolikelihood, along with a definition of an appropriate joint distribution and sampling mechanism even when the conditionals are not consistent. GSNs can be used with missing inputs and can be used to sample subsets of variables given the rest. We validate these theoretical results with experiments on two image datasets using an architecture that mimics the Deep Boltzmann Machine Gibbs sampler but allows training to proceed with simple backprop, without the need for layerwise pretraining.
MIDA: Multiple Imputation Using Denoising Autoencoders

Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness proportions and distributions. Evaluation on several real life datasets show our proposed model significantly outperforms current state-of-the-art methods under varying conditions while simultaneously improving end of the line analytics.
Neural correlates of dispositional mindfulness during affect labeling.

Mindfulness is a process whereby one is aware and receptive to present moment experiences. Although mindfulness-enhancing interventions reduce pathological mental and physical health symptoms across a wide variety of conditions and diseases, the mechanisms underlying these effects remain unknown. Converging evidence from the mindfulness and neuroscience literature suggests that labeling affect may be one mechanism for these effects.



Participants (n = 27) indicated trait levels of mindfulness and then completed an affect labeling task while undergoing functional magnetic resonance imaging. The labeling task consisted of matching facial expressions to appropriate affect words (affect labeling) or to gender-appropriate names (gender labeling control task).



After controlling for multiple individual difference measures, dispositional mindfulness was associated with greater widespread prefrontal cortical activation, and reduced bilateral amygdala activity during affect labeling, compared with the gender labeling control task. Further, strong negative associations were found between areas of prefrontal cortex and right amygdala responses in participants high in mindfulness but not in participants low in mindfulness.



The present findings with a dispositional measure of mindfulness suggest one potential neurocognitive mechanism for understanding how mindfulness meditation interventions reduce negative affect and improve health outcomes, showing that mindfulness is associated with enhanced prefrontal cortical regulation of affect through labeling of negative affective stimuli.
NewsCube: delivering multiple aspects of news to mitigate media bias

The bias in the news media is an inherent flaw of the news production process. The resulting bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as Iraq war. It is very difficult, if not impossible, for readers to have penetrating views on realities against such bias. This paper presents NewsCube, a novel Internet news service aiming at mitigating the effect of media bias. NewsCube automatically creates and promptly provides readers with multiple classified viewpoints on a news event of interest. As such, it effectively helps readers understand a fact from a plural of viewpoints and formulate their own, more balanced viewpoints. While media bias problem has been studied extensively in communications and social sciences, our work is the first to develop a news service as a solution and study its effect. We discuss the effect of the service through various user studies.
Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment

The use of topic models to analyze domainspecific texts often requires manual validation of the latent topics to ensure that they are meaningful. We introduce a framework to support such a large-scale assessment of topical relevance. We measure the correspondence between a set of latent topics and a set of reference concepts to quantify four types of topical misalignment: junk, fused, missing, and repeated topics. Our analysis compares 10,000 topic model variants to 200 expertprovided domain concepts, and demonstrates how our framework can inform choices of model parameters, inference algorithms, and intrinsic measures of topical quality.
Combing Haar and MBLBP features for face detection using multi-exit asymmetric boosting

This paper proposes a visual face detection framework that enables fast image processing while achieving high detection rates. The proposed framework combines both multi block local binary pattern (MBLBP) features and Haar- like features using multi-exit asymmetric boosting for robust face detection. In this framework, the integral image is utilized to facilitate rapid extraction for both the MBLBP features and the Haar-like features. Experimental results showed that combing MBLBP and Haar-like features can achieve better detection rate than each of them can do individually.
Bayesian Statistics and Marketing

Bayesian methods have become widespread in marketing literature. We review the essence of the Bayesian approach and explain why it is particularly useful for marketing problems. While the appeal of the Bayesian approach has long been noted by researchers, recent developments in computational methods and expanded availability of detailed marketplace data has fueled the growth in application of Bayesian methods in marketing. We emphasize the modularity and flexibility of modern Bayesian approaches. The usefulness of Bayesian methods in situations in which there is limited information about a large number of units or where the information comes from different sources is noted. We include an extensive discussion of open issues and directions for future research.
- LEVEL ACCURACY WITH 50 X FEWER PARAMETERS AND < 0 . 5 MB MODEL SIZE

Recent research on deep convolutional neural networks (CNNs) has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple CNN architectures that achieve that accuracy level. With equivalent accuracy, smaller CNN architectures offer at least three advantages: (1) Smaller CNNs require less communication across servers during distributed training. (2) Smaller CNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller CNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small CNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques, we are able to compress SqueezeNet to less than 0.5MB (510× smaller than AlexNet). The SqueezeNet architecture is available for download here: https://github.com/DeepScale/SqueezeNet
Arm/trunk motion generation for humanoid robot

This paper develops two motion generation methods for the upper body of humanoid robots based on compensating for the yaw moment of whole body during motion. These upper body motions can effectively solve the stability problem of feet spin for robot walk. We analyze the ground reactive torque, separate the yaw moment as the compensating object and discuss the effect of arms swinging on whole body locomotion. By taking the ZMP as the reference point, trunk spin motion and arms swinging motion are generated to improve the biped motion stability, based on compensating for the yaw moment. The methods are further compared from the energy consumption point of view. Simulated experimental results validate the performance and the feasibility of the proposed methods.
Recall Traces: Backtracking Models for Efficient Reinforcement Learning

In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. To this end, we advocate for the use of a backtracking model that predicts the preceding states that terminate at a given high-reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and samples which (state, action)-tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both onand off-policy RL algorithms across several environments and tasks.
Spaced Repetition Promotes Efficient and Effective Learning : Policy Implications for Instruction

Concern that students in the United States are less proficient in mathematics, science, and reading than their peers in other countries has led some to question whether American students spend enough time in school. Instead of debating the amount of time that should be spent in school (and on schoolwork), this article addresses how the available instructional time might be optimally utilized via the scheduling of review or practice. Hundreds of studies in cognitive and educational psychology have demonstrated that spacing out repeated encounters with the material over time produces superior long-term learning, compared with repetitions that are massed together. Also, incorporating tests into spaced practice amplifies the benefits. Spaced review or practice enhances diverse forms of learning, including memory, problem solving, and generalization to new situations. Spaced practice is a feasible and cost-effective way to improve the effectiveness and efficiency of learning, and has tremendous potential to improve educational outcomes. The article also discusses barriers to adopting spaced practice, recent developments, and their possible implications.
WIDER FACE: A Face Detection Benchmark

Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and the real world requirements. To facilitate future face detection research, we introduce the WIDER FACE dataset1, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER FACE dataset is an effective training source for face detection. We benchmark several representative detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated.
SWRL Rule Editor - A Web Application as Rich as Desktop Business Rule Editors

The Semantic Web Rule Language (SWRL) allows the combination of rules and ontology terms, defined using the Web Ontology Language (OWL), to increase the expressiveness of both. However, as rule sets grow, they become difficult to understand and error prone, especially when used and maintained by more than one person. If SWRL is to become a true web standard, it has to be able to handle big rule sets. To find answers to this problem, we first surveyed business rule systems and found the key features and interfaces they used and then, based on our finds, we proposed techniques and tools that use new visual representations to edit rules in a web application. They allow error detection, rule similarity analysis, rule clustering visualization and atom reuse between rules. These tools are implemented in the SWRL Editor, an open source plug-in for Web-Protégé (a web-based ontology editor) that leverages Web-Protégé’s collaborative tools to allow groups of users to not only view and edit rules but also comment and discuss about them. We evaluated our solution comparing it to the only two SWRL editor implementations openly available and showed that it implements more of the key features present in traditional rule systems.
Darwin, deception, and facial expression.

Darwin did not focus on deception. Only a few sentences in his book mentioned the issue. One of them raised the very interesting question of whether it is difficult to voluntarily inhibit the emotional expressions that are most difficult to voluntarily fabricate. Another suggestion was that it would be possible to unmask a fabricated expression by the absence of the difficult-to-voluntarily-generate facial actions. Still another was that during emotion body movements could be more easily suppressed than facial expression. Research relevant to each of Darwin's suggestions is reviewed, as is other research on deception that Darwin did not foresee.
ARTT taxonomy and cyber-attack Framewok

The security of computer systems has become essential especially in front of the critical issues of cyber-attacks that can result in the compromise of these systems because any act on a system intends to harm one of the security properties (Confidentiality, Integrity and availability). Studying computer attacks is also essential to design models of attacks aiming to protect against these attacks by modeling the last ones. The development of taxonomies leads to characterize and classify the attacks which lead to understand them. In computer's security taxonomy, we can have two broad categories: cyber-attack's taxonomy and cyber-security taxonomy. In this paper, we propose taxonomy for cyber-attacks according to an attacker vision and the aspect of achieving an attack. This taxonomy is based on 4 dimensions: Attack vector, Result, Type and Target. To generalize our approach, we have used the framework of the Discrete EVent system Specification DEVS. This framework depicts the overall vision of cyber-attacks. To partially validate our work, a simulation is done on a case study of buffer overflow. A DEVS model is described and a simulation is done via this formalism. This case study aims to reinforce our proposal.
Robot life-long task learning from human demonstrations: a Bayesian approach

ix Chapter 1: Introduction 1 1.1 Knowledge Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Human-Robot Communication . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Life-Long Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  Learning from Demonstration . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 Demonstration Approaches .  Life-Long Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Chapter 3: Learning from Demonstration 19 3.1 Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Role of the Instructor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3 Role of the Student . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.4 Knowledge Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.4.1 Human-Robot Communication . . . . . . . . . . . . . . . . . . . 24 3.4.2 System Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.5 Learning a Task Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Detection of potato diseases using image segmentation and multiclass support vector machine

Modern phenotyping and plant disease detection provide promising step towards food security and sustainable agriculture. In particular, imaging and computer vision based phenotyping offers the ability to study quantitative plant physiology. On the contrary, manual interpretation requires tremendous amount of work, expertise in plant diseases, and also requires excessive processing time. In this work, we present an approach that integrates image processing and machine learning to allow diagnosing diseases from leaf images. This automated method classifies diseases (or absence thereof) on potato plants from a publicly available plant image database called ‘Plant Village’. Our segmentation approach and utilization of support vector machine demonstrate disease classification over 300 images with an accuracy of 95%. Thus, the proposed approach presents a path toward automated plant diseases diagnosis on a massive scale.
Semi-supervised Conditional GANs

We introduce a new model for building conditional generative models in a semisupervised setting to conditionally generate data given attributes by adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model uses a pair of stacked discriminators to learn the marginal distribution of the data, and the conditional distribution of the attributes given the data respectively. In the semi-supervised setting, the marginal distribution (which is often harder to learn) is learned from the labeled + unlabeled data, and the conditional distribution is learned purely from the labeled data. Our experimental results demonstrate that this model performs significantly better compared to existing semi-supervised conditional GAN models.
Occlusion in augmented reality

Augmented reality in video-based display simply overlay virtual objects on real environment. In many cases this does not represent the actual situation in AR scene. When a real object supposes to occlude a virtual object, the augmented image may cause confusion in users' perception. This incorrect display contributes to misconceptions and wrong operations of task amongst users. The scenario of occlusion in AR application is discussed in this paper. Next, the main method used by other researchers to resolve occlusion problem was discussed. This is followed by a discussion about dependency factors and characteristics that influence the selection of occlusion handling approach and a comparison between a model-based and depth-based approach. Finally, a conclusion and proposed work are presented.
Threat Intelligence Computing

Cyber threat hunting is the process of proactively and iteratively formulating and validating threat hypotheses based on security-relevant observations and domain knowledge. To facilitate threat hunting tasks, this paper introduces threat intelligence computing as a new methodology that models threat discovery as a graph computation problem. It enables efficient programming for solving threat discovery problems, equipping threat hunters with a suite of potent new tools for agile codifications of threat hypotheses, automated evidence mining, and interactive data inspection capabilities. A concrete realization of a threat intelligence computing platform is presented through the design and implementation of a domain-specific graph language with interactive visualization support and a distributed graph database. The platform was evaluated in a two-week DARPA competition for threat detection on a test bed comprising a wide variety of systems monitored in real time. During this period, sub-billion records were produced, streamed, and analyzed, dozens of threat hunting tasks were dynamically planned and programmed, and attack campaigns with diverse malicious intent were discovered. The platform exhibited strong detection and analytics capabilities coupled with high efficiency, resulting in a leadership position in the competition. Additional evaluations on comprehensive policy reasoning are outlined to demonstrate the versatility of the platform and the expressiveness of the language.