Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Suggestions from a Physically Present Robot on Creative Generation

This study experimentally investigated the effect of suggestions from a physically present robot on human creative generation. In the experiments, we used a creative task in which the participants were required to draw creatures living on a planet other than the Earth, and a physically present robot, which provided suggestions for creative drawing to the participants with speech sounds and physical movements. First, the results of the pilot experiment conﬁrmed that drawing creativity was enhanced for the participants supported by a robot; however, they were unlikely to refer to the suggestions. Based on the re-sults, two hypotheses were developed: the suggestions from a robot offered a variety of different perspectives and facilitated metacognition (Hypothesis 1), and the suggestions worked as distractions and suppressed ﬁxated perspectives (Hypothesis 2). The experiment was conducted to investigate these hypotheses. As a result, Hypothesis 1 was supported. The results were discussed based on previous studies.
Inferring Structured Visual Concepts from Minimal Data

Humans can learn and reason about abstract concepts quickly, flexibly, and often from very little data. Here, we study how people learn novel concepts within a binary grid domain, and find that even this minimal task nonetheless necessitates the inference of highly structured parts as well as their compositional relationships. Furthermore, by changing the presentation condition of the learning examples, we reveal different approaches involved in learning such visual concepts: given the same images, human generalizations differ between rapid and static presentation conditions. We investigate this difference by developing several computational models that vary in their use of structured primitives and composition. We find that learning in the rapid presentation condition is best described as inference in simple models, while learning in the static presentation condition is best described as inference in a more structured space of graphics programs.
A Bayesian model of memory in a multi-context environment

In a noisy but structured world, memory can be improved by enhancing limited stimulus-speciﬁc memory with statistical information about the context. To do this, people have to learn the statistical structure of their current environment. We present a Sequential Monte Carlo (particle ﬁlter) model of how people track the statistical properties of the environment across multiple contexts. This model approximates non-parametric Bayesian clustering of percepts over time, capturing how people impute structure in their perceptual experience in order to more efﬁciently encode that experience in memory. Each trial is treated as a draw from a context-speciﬁc distribution, where the number of contexts is unknown (and potentially inﬁnite). The model maintains a ﬁnite set of hypotheses about how the percepts encountered thus far are assigned to contexts, updating these in parallel as each new percept comes in. We apply this model to a recall task where subjects had to recall the position of dots (Robbins, Hemmer, & Tang, 2014). Unbeknownst to subjects, each dot appeared in one of a few pre-deﬁned regions on the screen. Our model captures subjects’ ability to learn the inventory of contexts, the statistics of dot positions within each context, and the statistics of transitions between contexts—as reﬂected in both recall and prediction.
What's Lagging in our Understanding of Interruptions?: Effects of Interruption Lags in Sequential Decision-Making

Interruptions are an inevitable part of every day life. Previous research suggests that interruptions can decrease performance and increase errors and response time. Additionally, there is evidence that providing a lag time prior to an interruption can mitigate some of the interruption costs. The goal of this pa- per is to investigate the effects of interruptions and interruption lags and explore possible strategies to attenuate interrup- tion costs. A novel sequential decision-making paradigm was used, where the difﬁculty of the task and type of interruption were the two experimental manipulations. The results indicate that there is a potential beneﬁt to including a lag time when presented with interruptions.
Semantic influences on episodic memory distortions

Prior knowledge has long been known to shape new episodic memories. However, it is less clear how prior knowledge can scaffold the learning of a new class of information, and how this can bias memory for the episodes that contributed to its acquisition. We aimed to quantify distortions in episodic memories resulting from the use of prior category knowledge to facilitate learning new information. Across 4 experiments, participants encoded and retrieved image-location associations. Most members of a category (e.g., birds) were located near each other, such that participants could leverage their prior category knowledge to learn the spatial locations of categories as they encoded specific image locations. Critically, some typical and atypical category members were in random locations. We decomposed location memory into 2 measures: error, a measure of episodic specificity; and bias toward other category members, a measure of the influence of newly-learned information about category locations. First, we found that location memory was more accurate for images whose locations were spatially consistent with their category membership. Second, when images were spatially inconsistent (i.e., in random locations), retrieval of typical category members was more biased toward their category's location relative to atypical ones. These effects replicated across 3 experiments, disappeared when images were not arranged by category, and were stronger than effects observed with images arranged by visual similarity rather than category membership. Our observations provide compelling evidence that memory is a reconstruction of multiple sources of prior knowledge, new learning, and memory for specific events. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Using Event Graph to Improve Question Answering in E-commerce Customer Service

AliMe is an intelligent assistant that offers question answering service in the E-commerce customer service field. By representing knowledge as question answer (QA) pairs, AliMe is able to serve millions of customer questions per day and address 90%+ of them. However, in regulation-oriented scenarios, questions of type “why”, “whether”, “what if ” and “how next” often require knowledge reasoning to obtain a specific or precise answer, and QA-style knowledge representation turns out to be insufficient. To enable AliMe to better understand and serve customer questions, we propose to represent regulation knowledge as event graph, design systematic approach to map customer questions to events, and perform reasoning on event graph according to business rules. We launch our new system in the “counterfeiting penalty” scenario. Online results suggest that our new approach is able to gain better resolution.
Egocentric Tendencies in Theory of Mind Reasoning: An Empirical and Computational Analysis

Humans develop an ability for Theory of Mind (ToM) by the age of six, which enables them to infer another agent’s mental state and to differentiate it from one’s own. Much evidence suggests that humans can do this in a presumably optimal way and, correspondingly, a Bayesian Theory of Mind (BToM) framework has been shown to match human inferences and attributions. Mostly, this has been investigated with specific, explicit mentalizing tasks. However, other research has shown that humans often deviate from optimal reasoning in various ways. We investigate whether typical BToM models really capture human ToM reasoning in tasks that solicit more intuitive reasoning. We present results of an empirical study where humans deviate from Bayesian optimal reasoning in a ToM task but instead exhibit egocentric tendencies. We also discuss how computational models can better account for such sub-optimal processing.
Exploring the Representation of Linear Functions

Function learning research has highlighted the importance of human inductive biases that facilitate long-range extrapolations. However, most previous research is focused on aggregate errors or single-criterion extrapolations. Thus, little is known about the underlying psychological space in which continuous relationships are represented. We ask whether people can learn the distributional properties of new classes of relationships, using Markov Chain Monte Carlo with People, and find that (1) people are able to track not just the expected parameters of a linear function, but information about the variability of functions in a specific context and (2) in many cases these spaces over parameters exhibit multiple modes.
Limit Cycles and Bifurcations in a Class of Discontinuous Piecewise Linear Systems

In this paper, we consider planar piecewise linear differential systems with a line of discontinuity sharing a linear part. We study not only the number of crossing limit cycles, but also the number of sliding ones, and the coexistence of two configurations of limit cycles. In particular, we proved that both numbers of crossing limit cycles and sliding ones are at most [Formula: see text], but the total number of limit cycles is at most [Formula: see text]. Finally, by complete analysis on the number of limit cycles, we show some bifurcations which exist in generic Filippov systems, revealing also two nongeneric bifurcations.
Application of Granger Causality in Decoding Covert Selective Attention with Human EEG

Electroencephalography (EEG)-based BCIs have experienced a significant growth in recent years, especially the passive Brain Computer Interfaces (BCIs) with a wide application in the detection of cognitive and emotional states. But it is still unclear whether more subtle states, e.g., covert selective attention can be decoded with EEG signals. Here we used a behavioral paradigm to introduce the shift of selective attention between the visual and auditory domain. With EEG signals, we extracted features based on Grange Causality (GC) and successfully decoded the attentional shift through a support vector machine (SVM) based classifier. The decoding accuracy was significantly above the chance level for all 8 subjects tested. The features based on GC were further analyzed with tree-based feature importance analysis and recursive feature elimination (RFE) method to search for the optimal features for classification. Our work demonstrate that specific patterns of brain activities reflected by GC can be used to decode subtle state changes of the brain related to cross-modal selective attention, which opens new possibility of using passive BCIs in sophisticated perceptual and cognitive tasks.
An Approach for Recognition of Enhancer-promoter Associations based on Random Forest

Enhancers are sequences in the genome that regulate gene expression and are usually located far from transcription start sites. Enhancers regulate gene expression by interacting with promoters. Therefore, the recognition of the association between enhancers and promoters is an important issue in the study of enhancer regulation. At present, computational methods to recognize the association between enhancers and promoters are mainly realized by designing machine learning methods based on the biological signals on the genome sequence. These recognition methods ignore evaluating the classification power of features, resulting in limited recognition performance. In this paper, the classification power of the feature signals near enhancers and promoters in the genome sequence was evaluated, and the features with strong classification power were picked up. This was conducive to improving the recognition accuracy. The correlation between enhancers and promoters was recognized by the random forest method. Compared with the five main recognition methods, the accuracy of the recognition method in this paper is higher.
A Self-Powered Wireless Sensor Network

SARSA is one of the most advanced algorithms in reinforcement learning and artificial intelligence, this algorithm lets the actor to learn about its environment and to act accordingly. Batteries are the main stopper for adopting wireless communications in the industry. Industrial processes are a source of waste energy. This paper explores the different components of the IIoT devices, which changes are needed in order to be powered by the energy on its surroundings, and finally, how those points above can be tackled using artificial intelligence.
Impact of Free Contributions on Private Benefits in Online Healthcare Communities

ABSTRACT Online healthcare communities (OHCs) have become increasingly popular and now impact the way that doctors connect with patients. In the OHCs, many doctors have become multitype service providers. Considering that doctors’ contributions to online free services and the private benefits of online paid services are both important, it is vital for platforms to encourage doctors’ online participation. Yet little is known about the spillover effects of the contributions to online free services on doctors’ private benefits regarding paid services. In our study, we investigate the relationship between online free services and the private benefits of online paid services based on the theory of the spillover effects and the signaling theory. We analyze a panel data set from 1,349 doctors participating in an OHCs in China. Our results indicate that participation in online free services significantly increases the private benefits of family doctors before a certain value. Moreover, a doctor’s ranking has a moderating effect on this relationship. Our results highlight the spillover effects of an online free service and make contributions to signaling theory, and they have important implications for doctors, OHCs platform, and governments.
Dark Forces in Language Comprehension: The Case of Neuroticism and Disgust in a Pupillometry Study

We report on initial findings from a pupillometry study that investigated the influence of two extra-linguistic variables, namely Neuroticism and Disgust Sensitivity, on auditory language comprehension in adults. Results suggest that: (1) Language comprehension is influenced by extra-linguistic variables and individual differences; (2) the processing of different kinds of linguistic errors, as opposed to clashes with an individual’s value or belief system, are influenced by different extra-linguistic variables; and that (3) Disgust Sensitivity at least partially predicts pupillary responses to utterances clashing with an individual’s belief system. Results are discussed with regards to linguistic anticipation, cognitive effort and arousal, and resource allocation.
Insulating Distributional Semantic Models from Catastrophic Interference

Predictive neural networks, such as word2vec, have seen impressive recent popularity as an architecture to learn distributional semantics in the fields of machine learning and cognitive science. They are particularly popular because they learn continuously, making them more space efficient and cognitively plausible than classic models of semantic memory. However, a major weakness of this architecture is catastrophic interference (CI): The sudden and complete loss of previously learned associations when encoding new ones. CI is an issue with backpropagation; when learning sequential data, the error signal dramatically modifies the connection weights between nodes—causing rapid forgetting of previously learned information. CI is a huge problem for predictive semantic models of word meaning, because multiple word senses interfere with each other. Here, we evaluate a recently proposed solution to CI from neuroscience, elastic weight consolidation, as well as a Hebbian learning architecture from the memory literature that does not produce an error signal. Both solutions are evaluated on an artificial and natural language task in their ability to insulate a previously learned sense of a word when learning a new one.
Automata-Based Generation of Test Cases for Reactive Systems

The definition of a formal specification is a fundamental step in the design of safety-critical systems. The specification defines the behaviors and constraints that the system under development is required to satisfy. Depending on the system dimension, the specification can be used to verify its correctness or the correctness of its abstract model. In some cases, the system can also be directly synthesized from the specification. However, when the system is too complex for a full verification or for synthesis, developers usually rely on testing to demonstrate the correct operation of the system. In this paper we present our ongoing work on automatic test cases generation, relying on Linear Temporal Logic (LTL) as a specification formalism. The presented algorithm is implemented in SpecPro, our library for supporting analysis and development of formal requirements in cyber-physical systems.
Combining Block-Based Programming and Hardware Prototyping to Foster Computational Thinking

The promotion of Computational Thinking principles at different levels of education enables the development of practical competencies for software development. Several off-the-shelf technologies exist to promote computational abilities with the support of block-based programming and working hardware. However, in some contexts, a significant limitation is the complexity and costs that carry the acquisition and implementation of working hardware such as Educational Robots. In this paper, we discuss a strategy to reinforce the learning of Computational Thinking (CT) skills by providing an analogous method to Educational Robots. We offer an affordable solution to assist high school students to build a CT-based software/hardware integrated solution. We executed this strategy in a coding bootcamp where students were required to design and implement an algorithm, and illustrate its execution in software and hardware. The proposed exercise delivered the expected results engaging students to ideate the solution of a problem, implement an algorithm, and create a physical demonstration of it.
Incremental Relaying for Power Line Communications: Performance Analysis and Power Allocation

In this paper, incremental decode-and-forward (IDF) and incremental selective decode-and-forward (ISDF) relaying are proposed to improve the spectral efficiency of power line communication. Contrary to the traditional decode-and-forward (DF) relaying, IDF and ISDF strategies utilize the relay only if the direct link ceases to attain a certain information rate, thereby improving the spectral efficiency. The path gain through the power line is assumed to be log-normally distributed with high distance-dependent attenuation and the additive noise is from a Bernoulli-Gaussian process. Closed-form expressions for the outage probability, and approximate closed-form expressions for the end-to-end average channel capacity and the average bit error rate for binary phase-shift keying are derived. Furthermore, a closed-form expression for the fraction of times the relay in use is derived as a measure of the spectral efficiency. Comparative analysis of IDF and ISDF with traditional DF relaying is presented. It is shown that the IDF is a specific case of ISDF and can obtain optimal spectral efficiency without compromising the outage performance. By employing power allocation to minimize the outage probability, it is realized that the power should be allocated in accordance with the inter-node distances and channel parameters.
The Effect for Category Learning on Recognition Memory: A Signal Detection Theory Analysis

Previous studies have shown that category learning affects subsequent recognition memory. However, questions remain as to how category learning affects discriminability during recognition. In this three-stage study, we employed sets of simulated flowers with categoryand non-category-inclusion features appearing with equal probabilities. In the learning stage, participants were asked to categorize flowers by identifying the category-inclusion feature. Next, in the studying stage, participants memorized a new set of flowers, a third of which belonged to the learned category. Finally, in the testing stage, participants received a recognition test with old and new flowers, some from the learned category, some from a not-learned category, some from both categories, and some from neither category. We applied hierarchical Bayesian signal detection theory models to recognition performance and found that prior category learning affected both discriminability as well as criterion bias. That is, people that learned the category well, exhibited improved discriminability and a shifted bias toward flowers from the learned relative to the not learned category.
How should we incentivize learning? An optimal feedback mechanism for educational games and online courses

There are plenty of opportunities for life-long learning but people rarely seize them. Game elements are an increasingly popular tool to keep students engaged in learning. But gamification only works when it is done properly. Here, we introduce the first principled approach to gamifying learning environments. Our feedback mechanism rewards students’ efforts and study choices according to how beneficial they are in the long run. The rewards are conveyed by game elements that we call “optimal brain points”. In our experiment, these optimal brain points significantly increased the proportion of participants who attempted to learn a difficult skill, persisted through failure, and succeeded to master it. Our method provides a principled approach to designing incentive structures and feedback mechanisms for both educational games and online courses. We are optimistic that this can help people overcome the motivational obstacles to self-directed life-long learning.
Symmetries of Quotient Networks for Doubly Periodic Patterns on the Square Lattice

Patterns of synchrony in networks of coupled dynamical systems can be represented as colorings of the nodes, in which nodes of the same color are synchronous. Balanced colorings, where nodes of the same color have color-isomorphic input sets, correspond to dynamically invariant subspaces, which can have a significant effect on the typical bifurcations of network dynamical systems. Orbit colorings for subgroups of the automorphism (symmetry) group of the network are always balanced, although the converse is false. We compute the automorphism groups of all doubly periodic quotient networks of the square lattice with nearest-neighbor coupling, and classify the “exotic” cases where this quotient network has extra automorphisms not induced by automorphisms of the square lattice. These comprise five isolated exceptions and two infinite families with wreath product symmetry. We also comment briefly on implications for bifurcations to doubly periodic patterns in square lattice models.
Exploring Fact-checked Claims and their Descriptive Statistics

ClaimsKG is a RDF knowledge graph of fact-checked claims and related metadata, such as their truth values, authors or dates. It gathers information from popular fact-checking websites, annotates claims with related entities from DBpedia, and lifts the data into RDF by using a dedicated RDFS model. We present two open source, user-friendly Web-platforms operating on top of ClaimsKG: (1) the ClaimsKG Explorer an engine to conduct ad-hoc/faceted search over the graph, and (2) the ClaimsKG Statistical Observatory-a tool allowing to extract and visualize detailed statistics of the ClaimsKG data.