Reviewerly/exHarmony huggingface dataset:
Young children outperform feed-forward and recurrent neural networks on challenging object recognition tasks

Unlike artificial neural networks (ANNs), human object recognition is robust in degraded conditions. Accumulating evidence suggests that recurrent connections within the ventral visual stream are necessary in such conditions. Indeed, incorporating recurrence within ANNs significantly improves their performance. Nevertheless, despite the success of recurrent ANNs over purely feedforward ones, the recognition abilities of these models on degraded objects lags far behind that of human adults. Why is the human visual system impervious to such conditions? In a novel approach to answering this question, we compared the recognition abilities of state-of-the-art ANNs to 4- and 5-year-old children. Although children show impressive object recognition abilities, it remains unknown how robust these abilities are when objects are degraded or under speeded conditions. Children (N = 84) were tested on a challenging object recognition task which required them to identify rapidly presented object outlines (100 ms - 300 ms; forward and backward masked) that had perturbed or illusory contours. We found that even the youngest children successfully identified both perturbed and illusory outlines at the fastest speeds, even though objects were both forward and backward masked. By contrast, neither a feedforward model (VGG19), nor a model that approximates recurrence (ResNet101), showed comparable performance to children. Thus, despite receiving exponentially more supervised object training than children (Zador, 2019), ANNs fall short of the recognition abilities of children. We suggest that, from early in development, robust object recognition in humans may be supported by parallel feedforward processes in the dorsal stream, in addition to recurrent processes in ventral stream.
Multi-robot Search Algorithm using Timed Random Switching of Exploration Approaches

This paper improves a previous solution to the search and rescue problem using a robotic swarm. The proposed algorithm uses timed random switching between two approaches in swarm exploration. The approaches use variable distance repulsion and fixed distance repulsion respectively and employ differing attitudes depending on observed exploration approach of neighbours. The comparison of results between the existing and proposed algorithm shows improvement when compared for target size, swarm size and environment area across all simulated scenarios.
Deep Inverse Rendering for Practical Object Appearance Scan with Uncalibrated Illumination

In this paper, we propose a practical method to estimate object appearance from an arbitrary number of images. We use a moving flashlight as light source, and encode surface reflectance properties in a pre-learned embedded latent space. Such lighting and appearance model combination enables our method to effectively narrow the solution space. Uncalibrated illumination requirement extremely simplifies our setup and affords it unnecessary to accurately locate light positions in advance. Moreover, our method automatically selects key frames before appearance estimation, which largely reduces calculation cost. Both synthetic and real experiments demonstrate that our method can recover object appearance accurately and conveniently.
Constrained Clustering via Post-processing

Constrained clustering has received much attention since its inception as the ability to add weak supervision into clustering has many uses. Most existing work is algorithm-specific, limited to simple together and apart constraints and does not attempt to satisfy all constraints. This limits applications including where satisfying all constraints is required such as fairness. In this work, we take the novel direction of post-processing the results of a clustering algorithm (constrained or unconstrained) as a combinatorial optimization problem to find the best allocation of instances to clusters whilst enforcing constraints. Experiments show that when evaluated on a ground truth, our method is competitive in terms of clustering quality with the more recent approaches while being more computationally efficient. Finally, since all constraints are satisfied, our work can be applied to areas such as fairness including both group level and individual level fairness.
Quantifying the Contextualization of Word Representations with Semantic Class Probing

Pretrained language models achieve state-of-the-art results on many NLP tasks, but there are still many open questions about how and why they work so well. We investigate the contextualization of words in BERT. We quantify the amount of contextualization, i.e., how well words are interpreted in context, by studying the extent to which semantic classes of a word can be inferred from its contextualized embedding. Quantifying contextualization helps in understanding and utilizing pretrained language models. We show that the top layer representations support highly accurate inference of semantic classes; that the strongest contextualization effects occur in the lower layers; that local context is mostly sufficient for contextualizing words; and that top layer representations are more task-specific after finetuning while lower layer representations are more transferable. Finetuning uncovers task-related features, but pretrained knowledge about contextualization is still well preserved.
Identifying critical outbreak time window of controversial events based on sentiment analysis

The response of netizens toward controversial events plays an important guiding role in the development of events. Based on the identification of such responses, this study aimed to determine the critical outbreak time window of events. The microblog texts related to an event were divided into seven emotional categories via multi-emotional analysis to capture the subtle emotions of netizens toward an event, i.e., public opinion. By detecting the characteristics of the text and regional coverage of emotions, an emotional coverage index that reflects the intensity of emotional impact was proposed to determine the mainstream emotion of netizens. By capturing the mutation characteristics of the impact intensity of mainstream emotions, the critical time window of the public opinion toward the event was obtained. The experimental results demonstrated that the proposed method can effectively identify the critical outbreak time window of controversial events, which can help authorities in preventing the further aggravation of events.
Polygamy relations of multipartite entanglement beyond qubits

We investigate the polygamy relations related to the concurrence of assistance for any multipartite pure states. General polygamy inequalities given by the $\alpha$th $(0\leq \alpha\leq 2)$ power of concurrence of assistance is first presented for multipartite pure states in arbitrary-dimensional quantum systems. We further show that the general polygamy inequalities can even be improved to be tighter inequalities under certain conditions on the assisted entanglement of bipartite subsystems. Based on the improved polygamy relations, lower bound for distribution of bipartite entanglement is provided in a multipartite system. Moreover, the $\beta$th ($0\leq \beta \leq 1$) power of polygamy inequalities are obtained for the entanglement of assistance as a by-product, which are shown to be tighter than the existing ones. A detailed example is presented.
DBOS: A Dialog-Based Object Query System for Hospital Nurses

Due to the advance of indoor positioning technology, it is now possible to trace mobile medical equipment (such as electrocardiography machines, patient monitors, and so on) being moved around a hospital ward. With the support of an object tracking system, nurses can easily locate and find a device, especially when they prepare for a shift change or a medical treatment. As nurses usually face high workloads, it is highly desirable to provide nurses with a user-friendly search interface integrated into a popular mobile app that they use daily. For this, DBOS, a dialog-based object query system, is proposed, which simulates a real conversation with users via the Line messaging app’s chatbot interface. A hybrid method that combines cosine similarity (CS) and term frequency–inverse document frequency (TF-IDF) is used to determine user intent. The result is returned to the user through Line’s interface. To evaluate the applicability of DBOS, 70 search queries given by a head nurse were tested. DBOS was compared with CS, TF-IDF, and Facebook Wit.ai respectively. The experiment results show that DBOS outperforms the abovementioned methods and can achieve a 92.8% accuracy in identifying user intent.
Adversarial Robustness of Supervised Sparse Coding

Several recent results provide theoretical insights into the phenomena of adversarial examples. Existing results, however, are often limited due to a gap between the simplicity of the models studied and the complexity of those deployed in practice. In this work, we strike a better balance by considering a model that involves learning a representation while at the same time giving a precise generalization bound and a robustness certificate. We focus on the hypothesis class obtained by combining a sparsity-promoting encoder coupled with a linear classifier, and show an interesting interplay between the expressivity and stability of the (supervised) representation map and a notion of margin in the feature space. We bound the robust risk (to $\ell_2$-bounded perturbations) of hypotheses parameterized by dictionaries that achieve a mild encoder gap on training data. Furthermore, we provide a robustness certificate for end-to-end classification. We demonstrate the applicability of our analysis by computing certified accuracy on real data, and compare with other alternatives for certified robustness.
Acquisition of<i>Z</i>-Number-Valued Clusters by Using a New Compound Function

A large number of clustering methods exist including deterministic, probabilistic, and fuzzy clustering. All these methods are devoted to handling different types of uncertainty. No studies have been encountered on clustering taking into account a confluence of probabilistic and fuzzy information. In the existing studies, the reliability of extracted knowledge is one of the important issues to be investigated. The concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z</i> -number arises as a formal construct that expresses reliability of information under bimodal distribution. In this article, we propose an approach to construction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z</i> -number-valued clusters of a dataset for evaluation of reliability of extracted data-driven knowledge. Real-world applications are given that confirm the usefulness of the proposed method.
Multi-purpose Low Latency Streaming Using Unmanned Aerial Vehicles

The usage of Unmanned Aerial Vehicles (UAVs) has become increasingly widespread over the years, rising to meet challenges of systems in several different sectors. The high mobility and ease of deployment of the UAV, combined with the distributed nature of UAV communication, enable many useful applications that are unique to these devices. In one of the most common UAV applications, the UAV is used to capture video and send it along to a server for processing, or to users for viewing. This video feed can be used in many sectors, from surveillance and surveying, to cultural event live streaming. It is even possible to implement advanced computer vision algorithms for more advanced features. There are many publications that consider these applications, however, few works implement a complete end-to-end system. In this work a complete low latency, scalable, end-to-end system, from UAV to user, is implemented, and several different use cases are considered and implemented.
One-Way Pioneer Guide Pity Beetle Algorithm: A New Evolutionary Algorithm for Solving Global Optimization Problems

To improve the convergence speed and accuracy of the Pity Beetle Algorithm(PBA), we propose an improved algorithm called one-way pioneer guide PBA (OPGPBA). First of all, given the specificity of the random sampling technology(RST), resulting in search holes, we propose a new rRST search mechanism, which integrates the random generation mode of the full-space traversal search capability. According to the algorithm's different requirements for neighborhood search, medium-scale search, large-scale search, and global search, the fixed-size mode factor is adaptively processed to make its size change adaptively with iteration. Finally, we propose a mechanism for the pioneer to guide population reproduction. The evolutionary direction of the population in each dimension is determined according to the change in the pioneer position, which effectively improves the convergence ability of the algorithm. Experiments on the CEC2013 test set show that the OPGPBA algorithm has apparent advantages in convergence speed and solution accuracy compared with the three excellent algorithms so far.
An anisotropic reference matrix for image steganography

A lot of image steganographic techniques (also called data hiding) conceal secret data by utilizing a reference matrix (RM), and some new types of RMs, such as the turtle shell, an octagon-shaped shell, the Sudoku table, and so on, have been proposed in recent years. In this article, we present a novel type of RM called anisotropic RM. By employing a full search strategy, we have found the optimal parameters for constructing the anisotropic RM. To judge the performance of a parameter set quickly, a theoretical peak signal-to-noise ratio (PSNR) evaluation method is proposed. In addition, we extend the RM to three-dimensional (3D) space. Experimental results show that our data hiding scheme, based on the anisotropic RM, has a better quality stego image than previous methods. Moreover, the 3D RM works better than the traditional 2D RM using the same embedding capacity.
$$\mathcal {C}$$o$$\mathcal {M}$$erger: A Customizable Online Tool for Building a Consistent Quality-Assured Merged Ontology

Merging ontologies enables the reusability and interoperability of existing knowledge. With growing numbers of relevant ontologies in any given domain, there is a strong need for an automatic, scalable multi-ontology merging tool. We introduce $$\mathcal {C}$$ o $$\mathcal {M}$$ erger, which covers four key aspects of the ontology merging field: compatibility checking of the user-selected Generic Merge Requirements (GMR)s, merging multiple ontologies with adjustable GMRs, quality assessment of the merged ontology, and inconsistency handling of the result. $$\mathcal {C}$$ o $$\mathcal {M}$$ erger is freely accessible through a live portal and the source code is publicly distributed.
An operator view of policy gradient methods.

We cast policy gradient methods as the repeated application of two operators: a policy improvement operator $\mathcal{I}$, which maps any policy $\pi$ to a better one $\mathcal{I}\pi$, and a projection operator $\mathcal{P}$, which finds the best approximation of $\mathcal{I}\pi$ in the set of realizable policies. We use this framework to introduce operator-based versions of traditional policy gradient methods such as REINFORCE and PPO, which leads to a better understanding of their original counterparts. We also use the understanding we develop of the role of $\mathcal{I}$ and $\mathcal{P}$ to propose a new global lower bound of the expected return. This new perspective allows us to further bridge the gap between policy-based and value-based methods, showing how REINFORCE and the Bellman optimality operator, for example, can be seen as two sides of the same coin.
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity

Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis. However, a flow-based network is considered to be inefficient in parameter complexity because of reduced expressiveness of bijective mapping, which renders the models unfeasibly expensive in terms of parameters. We present an alternative parameterization scheme called NanoFlow, which uses a single neural density estimator to model multiple transformation stages. Hence, we propose an efficient parameter decomposition method and the concept of flow indication embedding, which are key missing components that enable density estimation from a single neural network. Experiments performed on audio and image models confirm that our method provides a new parameter-efficient solution for scalable NFs with significant sublinear parameter complexity.
Discovering Reinforcement Learning Algorithms

Reinforcement learning (RL) algorithms update an agent's parameters according to one of several possible rules, discovered manually through years of research. Automating the discovery of update rules from data could lead to more efficient algorithms, or algorithms that are better adapted to specific environments. Although there have been prior attempts at addressing this significant scientific challenge, it remains an open question whether it is feasible to discover alternatives to fundamental concepts of RL such as value functions and temporal-difference learning. This paper introduces a new meta-learning approach that discovers an entire update rule which includes both 'what to predict' (e.g. value functions) and 'how to learn from it' (e.g. bootstrapping) by interacting with a set of environments. The output of this method is an RL algorithm that we call Learned Policy Gradient (LPG). Empirical results show that our method discovers its own alternative to the concept of value functions. Furthermore it discovers a bootstrapping mechanism to maintain and use its predictions. Surprisingly, when trained solely on toy environments, LPG generalises effectively to complex Atari games and achieves non-trivial performance. This shows the potential to discover general RL algorithms from data.
Using paired teaching for earthquake education in schools

Abstract. In this study, we have created 10 geoscience video lessons that follow the paired-teaching pedagogical approach. This method is used to supplement the standard school curriculum with video lessons, instructed by geoscientists from around the world, coupled with activities carried out under the guidance of classroom teachers. The video lessons introduce students to the scientific concepts behind earthquakes (e.g. the Earth's interior, plate tectonics, faulting, and seismic energy), earthquake hazards, and mitigation measures (e.g. liquefaction, structural, and non-structural earthquake hazards). These concepts are taught through hands-on learning, where students use everyday materials to build models to visualize basic Earth processes that produce earthquakes and explore the effects of different hazards. To evaluate the effectiveness of these virtual lessons, we tested our videos in school classrooms in Dushanbe (Tajikistan) and London (United Kingdom). Before and after the video implementations, students completed questionnaires that probed their knowledge on topics covered by each video, including the Earth's interior, tectonic plate boundaries, and non-structural hazards. Our assessment results indicate that, while the paired-teaching video lessons appear to enhance student knowledge and understanding of some concepts (e.g. Earth's interior, earthquake location forecasting, and non-structural hazards), they bring little change to their views on the causes of earthquakes and their relation to plate boundaries. In general, the difference between UK and Tajik students' level of knowledge prior to and after video testing is more significant than the difference between pre- and post-knowledge for each group. This could be due to several factors affecting curriculum testing (e.g. level of teachers' participation and classroom culture) and students' learning of content (e.g. pre-existing hazards knowledge and experience). To maximize the impact of school-based risk reduction education, curriculum developers must move beyond innovative content and pedagogical approaches, take classroom culture into consideration, and instil skills needed for participatory learning and discovery.
A HOLISTIC FRAMEWORK OF STE(A)M EDUCATORS COMPETENCES

N. Spyropoulou, A. KameasHellenic Open University / Computer Technology Institute and Press "Diophantus" (GREECE)
Left Ventricle Segmentation Based on a Dilated Dense Convolutional Networks

The automatic segmentation of the left ventricle in magnetic resonance (MR) images is the basis of computer-aided diagnosis systems.To accurately extract the endocardium and epicardium of the left ventricle from MR images, a method based on a dilated dense convolutional network (DDCN) has been proposed in this paper.First, to reduce memory consumption, computing time and the class imbalance between the target and background, a clustering algorithm that combines the prior knowledge of the spatial relationship between the slices has been proposed to crop the region of interest (ROI).Then, the DDCN model with 8 dilated convolutional layers and dense connections, which is efficient with respect to its memory consumption and training time, has been proposed to delineate the endocardium and epicardium.To compare the DDCN model with other algorithms, 30 sequences of the MICCAI 2009 left ventricle segmentation challenge database are used to train the proposed model and the other 15 sequences are used for testing.The performance of the proposed method is evaluated by the percentage of "good" contours (PGC), average Dice metric (ADM) and average perpendicular distance (APD).Our results show that for the endocardial and epicardial contours, the PGCs are 99.49%±1.99%and 100%±0%, the APDs are 1.50 ± 0.34 mm and 1.31 ± 0.22 mm, and the ADMs are 0.93 ± 0.03 and 0.96 ± 0.01, respectively, which indicates that our method provides contours with great agreement with the ground truth.In addition, the comparison results show that our method exhibits outstanding performance and possesses promising potential to be used in computer-aided diagnosis systems for cardiovascular disease.
Morphological convergence as on-line lexical analogy

The English past tense contains pockets of variation, where regular and irregular forms compete (e.g. learned/learnt, weaved/wove). Individuals vary considerably in the degree to which they prefer irregular forms. This article examines the degree to which individuals may converge on their regularization patterns and preferences. We report on a novel experimental methodology, using a cooperative game involving nonce verbs. Analysis of participants' postgame responses indicates that their behavior shifted in response to an automated co-player's preferences, on two dimensions. First, players regularize more after playing with peers with high regularization rates, and less after playing with peers with low regularization rates. Second, players' overall patterns of regularization are also affected by the particular distribution of (ir)regular forms produced by the peer.
Research Status and Prospects of Deep Learning in Medical Images

With the continuous innovation and development of artificial intelligence, the theoretical research on and application of deep learning, one of its branches, has also reached a certain height, and has become a research hotspot in all walks of life. In the medical field, traditional manual image reading and other medical image analysis methods have been unable to adapt to the sharp increase in the amount of impact data. Based on this, the combination of deep learning and medical imaging has eased this pressure. This article first briefly analyzes the relevant theories of deep learning, and focuses on its applications in medical image classification and recognition, medical image segmentation, and computer-aided diagnosis. Finally, the application of deep learning in medical images is prospected.