Reviewerly/exHarmony huggingface dataset:
Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic

Abstract In this work, we propose a new method for modeling human reasoning about objects’ similarities. We assume that similarity depends on perceived intensities of objects’ attributes expressed by natural language expressions such as low, medium, and high. We show how to find the underlying structure of the matrix with intensities of objects’ similarities in the factor-analysis-like manner. The demonstrated approach is based on fuzzy logic and set theory principles, and it uses only maximum and minimum operators. Similarly to classic eigenvector decomposition, we aim at representing the initial linguistic ordinal-scale (LOS) matrix as a max–min product of other LOS matrix and its transpose. We call this reconstructing matrix a neuromatrix because we assume that such a process takes place at the neural level in our brain. We show and discuss on simple, illustrative examples, how the presented way of modeling grasps natural way of reasoning about similarities. The unique characteristics of our approach are treating smaller attribute intensities as less important in making decisions about similarities. This feature is consistent with how the human brain is functioning at a biological level. A neuron fires and passes information further only if input signals are strong enough. The proposal of the heuristic algorithm for finding the decomposition in practice is also introduced and applied to exemplary data from classic psychological studies on perceived similarities between colors and between nations. Finally, we perform a series of simulation experiments showing the effectiveness of the proposed heuristic.
Credit Card Fraud Detection Based on Multilayer Perceptron and Extreme Learning Machine Architectures

Due to the increasing digitalization of banking services and the predominance of mobile banking applications, the rate of credit card payments is increasing every year, among billions of transactions identified as fraudulent. Data mining algorithms have played a fundamental role in detecting fraudulent transactions, through combating fraudster's attacks working around classical fraud prevention systems. In this paper, we try to detect fraudulent transactions using two artificial neural network classifiers, Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM), applied on the credit card fraud dataset. The performance of these classifiers is evaluated based on accuracy, recall, precision, and classification time. The results show that the accuracy of MLP and ELM classifiers achieves respectively 97.84% and 95.46%. Otherwise, ELM is very fast for predicting new fraudulent transactions.
A clustering-based symbiotic organisms search algorithm for high-dimensional optimization problems

This paper proposed a fast metaheuristic method for high-dimensional optimizations problem with only one-control parameter in the setting. Essentially, the innovation of the proposed method is to apply automatic k-means clustering on the initial solutions of symbiotic organisms search to create subpopulations. Only the selected elite solutions in each cluster to interact with one another across clusters in the proposed model. This new elite solution searching process can be considered as a combination of local and global searching based on the solution clusters. The proposed method was compared to six representative methods in 28 benchmark problems and 10 composition problems. Also, the proposed method was also compared with four clustering-based metaheuristic methods. The experimental results show that the proposed model is more efficient in its computation and has a better searching quality. For high-dimensional problems, the performances of the proposed method was compared with the original symbiotic organisms search up to 1000 dimensions. The results show that the proposed method can alleviate the dimensionality effect to produce better solution quality with relatively fast computation.
Revocable and Offline-Verifiable Self-Sovereign Identities

Identity management systems enable users (i.e., provers) to authenticate and provide attributes to verifiers by using certified credentials obtained from an authority. To accept such a credential, verifiers require information on whether the presented credentials are still valid or if they have been revoked. Up-to-date revocation information can be obtained from a revocation database; however, this requires that the verifier or prover is online. The problem becomes more interesting in the offline case when the prover (e.g., citizen) and verifier (e.g., police officer) do not have an Internet connection to query the revocation status of the presented credential (e.g., digital driver's license). In this paper, we extend the Self-Sovereign Identity (SSI) model to support both revocation as well as offline-verification. Our concept introduces attestations of validity for a point in time, which are issued by the SSI network for credentials that have not been revoked, i.e., added by authorized entities to a revocation list. The concept aims to be generic so that it can be used for various use cases, e.g., by giving users the control over the frequency of re-attestation. To show our concept's feasibility and practicality, we developed and evaluated an implementation that includes an efficient and privacy-preserving showing of credentials using noninteractive zero-knowledge proofs, all while being offline.
Privacy-preserving searchable encryption in the intelligent edge computing

In intelligent edge computing, the private data of users are partly or entirely outsourced to the intelligent edge nodes for processing. To enforce security and privacy on edge network, data owners encrypt their private data and outsource it. It is particularly important to carry out efficient search and update on encrypted data. In this paper, we propose a searchable scheme to solve these problems on the intelligent edge network. Specially, we first propose S-HashMap index structure to make data update efficiently and safely while promising multi-keyword fuzzy ciphertext retrieval. Secondly, to measure the similarity between the query vector and the index nodes, we utilize the secure k-nearest neighbor to calculate the Euclidean distance. In addition to eliminating the necessity of pre-defined dictionaries, we achieve efficient multi-keyword fuzzy search and index updating without increasing search complexity. At the same time, this paper makes a thorough theoretical analysis and simulation of the proposed scheme. Compared with other schemes, we demonstrate that our scheme has better security and efficiency.
Symbolic Logic Meets Machine Learning: A Brief Survey in Infinite Domains

The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp concerns itself with questions about the expressiveness of formal languages for capturing knowledge about the world, together with proof systems for reasoning from such knowledge bases. The learning camp attempts to generalize from examples about partial descriptions about the world. In AI, historically, these camps have loosely divided the development of the field, but advances in cross-over areas such as statistical relational learning, neuro-symbolic systems, and high-level control have illustrated that the dichotomy is not very constructive, and perhaps even ill-formed.
Unlearning Scanner Bias for MRI Harmonisation

Combining datasets is vital for increased statistical power, especially for neurological conditions where limited data is available. However, variance due to differences in acquisition protocol and hardware limits our ability to combine datasets. We propose an iterative training scheme based on domain adaptation techniques, aiming to create scanner-invariant features while simultaneously maintaining overall performance on the main task. We demonstrate this on age prediction, but expect that our proposed training scheme will be applicable to any feedforward network and classification or regression task. We show that not only can we harmonise three MRI datasets from different studies, but can also successfully adapt the training to work with very biased datasets. The training scheme should, therefore, be applicable to most real-world data scenarios, enabling harmonisation for the task of interest.
Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks

Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology. However, they require water-tight surface meshes as inputs. Furthermore, adding vessels radii information on their graph compartments restricts their alignment along vascular centerlines. Here, we propose a novel graphing scheme that works with relaxed input requirements and intrinsically captures vessel radii information. The proposed approach is based on deforming geometric graphs constructed within vascular boundaries. Under a laplacian optimization framework, we assign affinity weights on the initial geometry that drives its iterative contraction toward vessels centerlines. We present a mechanism to decimate graph structure at each run and a convergence criterion to stop the process. A refinement technique is then introduced to obtain final vascular models. Our implementation is available on https://github.com/Damseh/VascularGraph. We benchmarked our results with that obtained using other efficient and state-of-the-art graphing schemes, validating on both synthetic and real angiograms acquired with different imaging modalities. The experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. Furthermore, it surpasses other techniques in providing representative models that capture all anatomical aspects of vascular structures.
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward

Many cooperative multi-agent problems require agents to learn individual tasks while contributing to the collective success of the group. This is a challenging task for current state-of-the-art multi-agent reinforcement algorithms that are designed to maximize either the global reward of the team or the individual local rewards. The problem is exacerbated when either of the rewards is sparse leading to unstable learning. To address this problem, we present Decomposed Multi-Agent Deep Deterministic Policy Gradient (DE-MADDPG): a novel cooperative multi-agent reinforcement learning framework that simultaneously learns to maximize the global and local rewards. We evaluate our solution on the defensive escort team problem and show that our solution achieves better and more stable performance than the direct adaptation of the MADDPG algorithm.
Empirical Analysis Of Overfitting And Mode Drop In Gan Training

We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective. We show that when stochasticity is removed from the training procedure, GANs can overfit and exhibit almost no mode drop. Our results shed light on important characteristics of the GAN training procedure. They also provide evidence against prevailing intuitions that GANs do not memorize the training set, and that mode dropping is mainly due to properties of the GAN objective rather than how it is optimized during training.
Multi Branch Siamese Network For Person Re-Identification

To capture robust person features, learning discriminative, style and view invariant descriptors is a key challenge in person Re-Identification (re-id). Most deep Re-ID models learn single scale feature representation which are unable to grasp compact and style invariant representations. In this paper, we present a multi branch Siamese Deep Neural Network with multiple classifiers to overcome the above issues. The multi-branch learning of the network creates a stronger descriptor with fine-grained information from global features of a person. Camera to camera image translation is performed with generative adversarial network to generate diverse data and add style invariance in learned features. Experimental results on benchmark datasets demonstrate that the proposed method performs better than other state of the arts methods.
Affordances of a mobile learner‐generated tool for pupils’ English as a second language vocabulary learning: An ecological perspective

Abstract This paper reports on a case study of English as a second language (ESL) vocabulary learning that examined a group of Grade 4 students’ mobile learning experiences of bridging in‐class and real‐life vocabulary learning mediated by a mobile learner‐generated content (m‐LGC) tool. This study was premised on the proposed framework of mobile vocabulary learning from an ecological perspective using the concept of affordances, taking the topic of “Food” as an example. Data collection included learning logs created by students and a questionnaire about students’ perceptions of the m‐LGC tool for vocabulary learning. The results revealed four types of affordances of the m‐LGC tool that the learners perceived and acted on in real life outside the classroom, namely: vocabulary form‐meaning mapping, consolidating vocabulary by productive use, vocabulary revision and social affordance. The students’ perceptions of the m‐LGC tool for vocabulary learning were positive. This study shows that the various affordances helped students in applying newly learned words to real‐life contexts. Social affordance provided the social contexts for communication via peer interaction, vocabulary expansion as well as the creation of a learning community.
A calibration method of computer vision system based on dual attention mechanism

Nowadays, the technology of using computer vision to calibrate objects is widely used, which has a huge market demand in many fields. This paper provides a calibration method of computer vision system based on dual attention neural network. This paper uses the camera to simulate human eyes to obtain three-dimensional images. After obtaining the three-dimensional images, the images are input into the Residual Network (ResNet) model, and the weight of ResNet is repeatedly updated so as to accurately identify the images. On this basis, introduces dual attention mechanism that an algorithm is used in natural language to the visual image processing, using multistage feature extraction method to extract the three-dimensional image for each characteristic of regional. After extracting the feature area, the accuracy of the feature area is constantly updated to the minimum. Besides, the feature areas are brought into the calibration algorithm of Zhang Zhengyou system to obtain the spatial coordinates of the objects in the attention area. This method can realize the space position calibration of specific objects under various complex backgrounds and calculate the distance from the calibrated objects, which can not only calibrate the system but also identify it, and greatly improve the reliability and accuracy of the calibration process.
Divide and Slide: Layer-Wise Refinement for Output Range Analysis of Deep Neural Networks

In this article, we present a layer-wise refinement method for neural network output range analysis. While approaches such as nonlinear programming (NLP) can directly model the high nonlinearity brought by neural networks in output range analysis, they are known to be difficult to solve in general. We propose to use a convex polygonal relaxation (overapproximation) of the activation functions to cope with the nonlinearity. This allows us to encode the relaxed problem into a mixedinteger linear program (MILP), and control the tightness of the relaxation by adjusting the number of segments in the polygon. Starting with a segment number of 1 for each neuron, which coincides with a linear programming (LP) relaxation, our approach selects neurons layer by layer to iteratively refine this relaxation. To tackle the increase of the number of integer variables with tighter refinement, we bridge the propagation-based method and the programming-based method by dividing and sliding the layerwise constraints. Specifically, given a sliding number s, for the neurons in layer l, we only encode the constraints of the layers between l - s and l. We show that our overall framework is sound and provides a valid overapproximation. Experiments on deep neural networks demonstrate significant improvement on output range analysis precision using our approach compared to the state-of-the-art.
Hydrogeochemical spatialization and controls of the Serra Geral Aquifer System in southern Brazil: A regional approach by self-organizing maps and k-means clustering

The aim of this study is to investigate the regional hydrogeochemical spatialization and controls of the Serra Geral Aquifer System (SGAS), a transboundary fractured aquifer, across the southern region of Brazil. An extensive dataset of 1564 groundwater wells represented by 16 attributes was analyzed to identify spatial patterns and groups with similar hydrogeochemical facies. An unsupervised machine learning approach, self-organizing maps (SOM), was used in combination with k-means clustering to carry out the analysis. SOM produces two-dimensional representations, allowing visual interpretation of nonlinear relationships between the attributes. The stochastic Davies-Bouldin index pointed out to an optimal number of four clusters, highlighting significant differences in geochemistry. Cluster 1 is the most abundant and widespread, corresponding to meteoric recharge; cluster 2 is influenced by the weathering of basaltic rocks, being widely distributed, but with a higher density at the southeastern region; cluster 3 is recognized as a mixing between all the other groups, with a sparse distribution, mostly in the extreme north of the area; cluster 4 is dominated by ascending flow from the underlying sedimentary aquifers, occurring in restricted areas. This study shows that SOM can identify large-scale spatial hydrogeochemical patterns of the SGAS driven by structural and stratigraphic elements.
Rethinking Background And Foreground In Deep Neural Network-Based Background Subtraction

Recently, deep neural networks have demonstrated excellent performance in foreground segmentation tasks such as moving object detection and change detection tasks. Various types of neural networks have been proposed, however, the previous works mainly discuss the accuracy. Analytics of the neural networks is important to utilize them effectively and improve their performance. In this paper, we investigate a foreground segmentation network and background subtraction network. In our analysis, we discuss differences of behaviors of the two networks in specific scenes and feature distributions in each layer of a background subtraction network to investigate feature learning. In addition, we provide suggestions about the comparison with these networks.
Understanding of Curved Corridor Scenes Based on Projection of Spatial Right-Angles

Helping mobile robots understand curved corridor scenes has considerable value in computer vision. However, due to the diversity of curved corridor scenes, such as curved structures that do not satisfy Manhattan assumption, understanding them remains a challenge. Curved non-Manhattan structures can be seen as compositions of spatial right angles projected into two dimensional projections, which may help us estimate their original posture in 3D scenes. In this paper, we presented an approach for mobile robots to understand curved corridor scenes including Manhattan and curved non-Manhattan structures, from a single image. Angle projections can be assigned to different clusters via geometric inference. Then coplanar structures can be estimated. Fold structures consisting of coplanar structures can be estimated, and curved non-Manhattan structures can be approximately represented by fold structures. Based on understanding curved non-Manhattan structures, the method is practical and efficient for a navigating mobile robot in curved corridor scenes. The algorithm requires no prior training or knowledge of the camera's internal parameters. With geometric features from a monocular camera, the method is robust to calibration errors and image noise. We compared the estimated curved layout against the ground truth and measured the percentage of pixels that were incorrectly classified. The experimental results showed that the algorithm can successfully understand curved corridor scenes including both Manhattan and curved non-Manhattan structures, meeting the requirements of robot navigation in a curved corridor environment.
<i>eWASM</i>: Practical Software Fault Isolation for Reliable Embedded Devices

As we connect more microcontrollers to the Internet and employ them to control the physical world around us, their reliability and security are increasingly important. Many microcontrollers provide limited facilities for hardware isolation, and real-time OSes offer custom APIs, that require coupling applications into the ecosystem and abstractions of that specific OS to leverage isolation. This article investigates the use of software sandboxing of applications to support isolation for resource-constrained devices. Toward this, we detail the design of eWASM, a processes abstraction that adapts a popular sandbox, Wasm, for microcontrollers. eWASM provides a runtime to constrain memory accesses and control flow, enabled by our aWsm Wasm compiler. We discuss and evaluate its multiple implementations that effectively trade time and space, optimizing for the constraints of embedded systems. This enables popular languages (e.g., C) to be effectively sandboxed by software. We demonstrate performance within 40% of native C on Polybench. We believe this is a practical and compelling result for many IoT domains, and it represents the first compiled sandboxing environment for microcontrollers. We show that restrictions of the current Wasm specification lead to significant memory consumption and provide suggestions for the creation of an embedded-specific Wasm variant.
Word saber

In this paper, we introduce Word Saber, a virtual reality (VR) edu-game for vocabulary learning. The game design and game mechanics are inspired by Beat Saber, where players have to cut virtual cubes from predefined directions by using bi-manual interactions with two lightsabers. Word Saber uses a similar concept, but players have to cut the corresponding object to the presented word. We evaluated the effects of Word Saber on learning efficiency and enjoyment in comparison to a traditional vocabulary learning method. The results show that Word Saber is effective for vocabulary learning. However, as Word Saber presented significantly lower scores in terms of recognition and recall rate compared to a flashcard method, it has lower efficiency. Regarding subjective preferences, our findings suggest that VR edu-games can be fun and useful for language learning in the long run as participants reported to be more motivated to use Word Saber afterward.