Reviewerly/exHarmony huggingface dataset:
Single-query Path Planning Using Sample-Efficient Probability Informed Trees

In this work, we present a novel sampling-based path planning method, called SPRINT. The method finds solutions for high dimensional path planning problems quickly and robustly. Its efficiency comes from minimizing the number of collision check samples. This reduction in sampling relies on heuristics that predict the likelihood that samples will be useful in the search process. Specifically, heuristics (1) prioritize more promising search regions; (2) cull samples from local minima regions; and (3) steer the search away from previously observed collision states. Empirical evaluations show that our method finds shorter or comparable-length solution paths in significantly less time than commonly used methods. We demonstrate that these performance gains can be largely attributed to our approach to achieve sample efficiency.
2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation

Brain tumour segmentation is a requirement of many quantitative MRI analyses involving glioma. This paper argues that 2D slice-wise approaches to brain tumour segmentation may be more compatible with current MRI acquisition protocols than 3D methods because clinical MRI is most commonly a slice-based modality. A 2D Dense-UNet segmentation model was trained on the BraTS 2020 dataset. Mean Dice values achieved on the test dataset were: 0.859 (WT), 0.788 (TC) and 0.766 (ET). Median test data Dice values were: 0.902 (WT), 0.887 (TC) and 0.823 (ET). Results were comparable to previous high performing BraTS entries. 2D segmentation may have advantages over 3D methods in clinical MRI datasets where volumetric sequences are not universally available.
Zero-Knowledge Proof-of-Identity: Sybil-Resistant, Anonymous Authentication on Permissionless Blockchains and Incentive Compatible, Strictly Dominant Cryptocurrencies

Zero-Knowledge Proof-of-Identity from trusted public certificates (e.g., national identity cards and/or ePassports; eSIM) is introduced here to permissionless blockchains in order to remove the inefficiencies of Sybil-resistant mechanisms such as Proof-of-Work (i.e., high energy and environmental costs) and Proof-of-Stake (i.e., capital hoarding and lower transaction volume). The proposed solution effectively limits the number of mining nodes a single individual would be able to run while keeping membership open to everyone, circumventing the impossibility of full decentralization and the blockchain scalability trilemma when instantiated on a blockchain with a consensus protocol based on the cryptographic random selection of nodes. Resistance to collusion is also considered.Solving one of the most pressing problems in blockchains, a zk-PoI cryptocurrency is proved to have the following advantageous properties:- an incentive-compatible protocol for the issuing of cryptocurrency rewards based on a unique Nash equilibrium- strict domination of mining over all other PoW/PoS cryptocurrencies, thus the zk-PoI cryptocurrency becoming the preferred choice by miners is proved to be a Nash equilibrium and the Evolutionarily Stable Strategy- PoW/PoS cryptocurrencies are condemned to pay the Price of Crypto-Anarchy, redeemed by the optimal efficiency of zk-PoI as it implements the social optimum- the circulation of a zk-PoI cryptocurrency Pareto dominates other PoW/PoS cryptocurrencies- the network effects arising from the social networks inherent to national identity cards and ePassports dominate PoW/PoS cryptocurrencies- the lower costs of its infrastructure imply the existence of a unique equilibrium where it dominates other forms of payment
Blended Learning via Schoology as a Learning Management System in Reading Class: Benefits and Challenges

Online learning integrated with face-to-face learning called blended learning is believed to be more useful than online learning or face-to-face learning itself. Schoology as a Learning Management System implemented in blended learning can help teachers to manage class information. Schoology allows students and teachers to share information and provides access to content or administrative features of certain courses. However, despite the benefits of blended learning via schoology mentioned, EFL teachers are still unwilling to exploit blended learning via Schoology in their reading classes. This literature study reviews several papers investigating the benefits and challenges of blended learning via Schoology as a Learning Management System. The purpose of the study is to observe the benefits and the challenges of blended learning via Schoology. The data were collected through primary and secondary resources. It is expected that the findings will boost the EFL teachers’ attention in applying blended learning via Schoology in their classes, and draw the attention of EFL teachers in covering the challenges of blended learning via Schoology to improve students’ learning experiences.&#x0D; Keywords: blended learning, Schoology, reading, benefits, challenges
Stacked Generalization Ensemble Method to Classify Bangla Handwritten Character

Image classification is one of the fundamental tasks in the field of machine learning. This study focuses on Bangla handwritten character recognition (BHCR). Recognizing handwritten characters has numerous applications like optical character recognition, post office automation, signboard recognition, number plate recognition, etc. A system that can classify Bangla handwriting on the character level efficiently on a large scale would surely be a primary step to achieve those autonomous user experiences. This study has developed a stacked generalization ensemble system that consists of six convolutional neural networks (CNN). The ensembled CNN model of this study has been able to perform more accurately than the individual CNN models. It can recognize 122 different Bangla handwritten characters with a test accuracy of 96.72% and an f1-score of 96.70%. The system has also outperformed most of the existing works by achieving better performance and by recognizing more handwritten characters.
Explainable AI in Credit Risk Management

Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen. For the finance sector, it provides great opportunities to enhance customer experience, democratize financial services, ensure consumer protection and significantly improve risk management. While it is easier than ever to run state-of-the-art machine learning models, designing and implementing systems that support real-world finance applications have been challenging. In large part because they lack transparency and explainability which are important factors in establishing reliable technology and the research on this topic with a specific focus on applications in credit risk management. In this paper, we implement two advanced post-hoc model agnostic explainability techniques called Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to machine learning (ML)-based credit scoring models applied to the open-access dataset offered by the US-based P2P Lending Platform, Lending Club. Specifically, we use LIME to explain instances locally and SHAP to get both local and global explanations. We discuss the results in detail and present multiple comparison scenarios by using various kernels available for explaining graphs generated using SHAP values. We also discuss the practical challenges associated with the implementation of these state-of-art eXplainabale AI (XAI) methods and document them for future reference. We have made an effort to document every technical aspect of this research, while at the same time providing a general summary of the conclusions.
ADMM-Based Adaptive Sampling Strategy for Nonholonomic Mobile Robotic Sensor Networks

This paper discusses the adaptive sampling problem in a nonholonomic mobile robotic sensor network for efficiently monitoring a spatial field. It is proposed to employ Gaussian process to model a spatial phenomenon and predict it at unmeasured positions, which enables the sampling optimization problem to be formulated by the use of the log determinant of a predicted covariance matrix at next sampling locations. The control, movement and nonholonomic dynamics constraints of the mobile sensors are also considered in the adaptive sampling optimization problem. In order to tackle the nonlinearity and nonconvexity of the objective function in the optimization problem we first exploit the linearized alternating direction method of multipliers (L-ADMM) method that can effectively simplify the objective function, though it is computationally expensive since a nonconvex problem needs to be solved exactly in each iteration. We then propose a novel approach called the successive convexified ADMM (SC-ADMM) that sequentially convexify the nonlinear dynamic constraints so that the original optimization problem can be split into convex subproblems. It is noted that both the L-ADMM algorithm and our SC-ADMM approach can solve the sampling optimization problem in either a centralized or a distributed manner. We validated the proposed approaches in 1000 experiments in a synthetic environment with a real-world dataset, where the obtained results suggest that both the L-ADMM and SC-ADMM techniques can provide good accuracy for the monitoring purpose. However, our proposed SC-ADMM approach computationally outperforms the L-ADMM counterpart, demonstrating its better practicality.
Applying particle swarm optimization-based decision tree classifier for wart treatment selection

Abstract Wart is a disease caused by human papillomavirus with common and plantar warts as general forms. Commonly used methods to treat warts are immunotherapy and cryotherapy. The selection of proper treatment is vital to cure warts. This paper establishes a classification and regression tree (CART) model based on particle swarm optimisation to help patients choose between immunotherapy and cryotherapy. The proposed model can accurately predict the response of patients to the two methods. Using an improved particle swarm algorithm (PSO) to optimise the parameters of the model instead of the traditional pruning algorithm, a more concise and more accurate model is obtained. Two experiments are conducted to verify the feasibility of the proposed model. On the hand, five benchmarks are used to verify the performance of the improved PSO algorithm. On the other hand, the experiment on two wart datasets is conducted. Results show that the proposed model is effective. The proposed method classifies better than k-nearest neighbour, C4.5 and logistic regression. It also performs better than the conventional optimisation method for the CART algorithm. Moreover, the decision tree model established in this study is interpretable and understandable. Therefore, the proposed model can help patients and doctors reduce the medical cost and improve the quality of healing operation.
Calibration method of the laser beam based on liquid lens for 3D precise measurement

The laser utilizing as an auxiliary equipment plays an important role in 3D precise measurement. However, the laser spot will inevitably be divergent with the increase of measurement range. To solve the problem, a calibration method of the laser beam based on liquid lens is proposed. And decentration and tilt will happen owing to installation of the liquid lens. Moreover, the focus of the laser beam can not be measured precisely in fact. Aiming at the problems mentioned above, optical path analysis of the laser beam with liquid lens is performed. To verify the correctness of the analysis, the simulation based on MATLAB and ZEMAX is performed. The experimental results show that straightness accuracy of the laser beam is 0.088 mm. And the RMSE of linear fitting of left and right laser beams are 0.013 mm and 0.035 mm within a 5 m distance respectively. Moreover, the RMSE of the spatial points is about 0.220 mm.
Dense-CNN: Dense convolutional neural network for stereo matching using multiscale feature connection

In spite of the fact that convolutional neural network-based stereo matching models have shown good performance in both accuracy and robustness, the issue of image feature loss in regions of texture-less, complex scenes and occlusions remains. In this paper, we present a dense convolutional neural network-based stereo matching method with multiscale feature connection, named Dense-CNN. First, we construct a novel densely connected network with multiscale convolutional layers to extract rich image features, in which the merged multiscale features with context information are utilized to estimate the cost volume for stereo matching. Second, we plan a novel loss-function strategy to learn the network parameters more reasonably, which can develop the performance of the proposed Dense-CNN model on disparity computation. Finally, we run our Dense-CNN model on the Middlebury and KITTI databases to conduct a comprehensive comparison with several state-of-the-art approaches. The experimental results demonstrate that the proposed method achieved superior performance on computational accuracy and robustness of disparity estimation, especially achieving the significant benefit of feature preservation in ill-posed regions.
Thinking Outside the Superbox

Designing a block cipher or cryptographic permutation can be approached in many different ways. One such approach, popularized by AES, consists in grouping the bits along the S-box boundaries, e.g., in bytes, and in consistently processing them in these groups. This aligned approach leads to hierarchical structures like superboxes that make it possible to reason about the differential and linear propagation properties using combinatorial arguments. In contrast, an unaligned approach avoids any such grouping in the design of transformations. However, without hierarchical structure, sophisticated computer programs are required to investigate the differential and linear propagation properties of the primitive. In this paper, we formalize this notion of alignment and study four primitives that are exponents of different design strategies. We propose a way to analyze the interactions between the linear and the nonlinear layers w.r.t. the differential and linear propagation, and we use it to systematically compare the four primitives using non-trivial computer experiments. We show that alignment naturally leads to different forms of clustering, e.g., of active bits in boxes, of two-round trails in activity patterns, and of trails in differentials and linear approximations.
10 years of video browser showdown

The Video Browser Showdown (VBS) has influenced the Multimedia community already for 10 years now. More than 30 unique teams from over 21 countries participated in the VBS since 2012 already. In 2021, we are celebrating the 10th anniversary of VBS, where 17 international teams compete against each other in an unprecedented contest of fast and accurate multimedia retrieval. In this tutorial we discuss the motivation and details of the VBS contest, including its history, rules, evaluation metrics, and achievements for multimedia retrieval. We talk about the properties of specific VBS retrieval systems and their unique characteristics, as well as existing open-source tools that can be used as a starting point for participating for the first time. Participants of this tutorial get a detailed understanding of the VBS and its search systems, and see the latest developments of interactive video retrieval.
TDRB: An Efficient Tamper-Proof Detection Middleware for Relational Database Based on Blockchain Technology

The relational database has become one of the mainstream tools for data storage and management.However, there are two main types of threats to relational databases: external attacks and internal tampering threats.In this paper, we focus on the internal tampering threats and propose a tamper-proof detection middleware named TDRB to provide efficient tamper-proof detection for relational databases.Within the TDRB middleware framework, raw data is still stored and queried from the relational database, while the hash digest of the critical data in the relational database is synchronously migrated to the blockchain for tamper detection.Based on this method, we leverage blockchain's immutability to detect data tamper and maintain the advanced features of relational databases to better support ease of data persistence, complex queries, and large storage capacity.We also propose a performance improvement mechanism that involves connecting the blockchain and relational database to improve throughput and mitigate performance impact.A series of experiments indicate that the TDRB middleware can accurately detect the tampering information during arbitrary tampering with the relational database and the cache database.Compare with the baseline, with the increase of cache hit rate, the TDRB middleware query speed increased by 93.1%, update speed increased by 16.3%, delete speed increased by 16.1%, and join operation average speed increased by 95.2%.Given its generality, the TDRB middleware can be flexibly and conveniently integrated into third-party platforms.
Language experience predicts semantic priming of lexical decision.

Computational models of semantic memory have been successful in accounting for a wide range of cognitive phenomena, including word categorization, semantic priming, and release from proactive interference. Conventionally, the texts input to these models have been curated to represent the average individual's language experience. While this approach has proven successful for making predictions that generalize across individuals, it prevents consideration of situations in which individuals have divergent semantic representations. The use of a representative corpus prevents the generation of predictions specific to the language experience of an individual. While this limitation has been discussed in the literature, previous investigations have not yet validated such corpus-specific predictions. I present an approach to generate corpus-specific semantic representations using internet news sites as corpora. I then validate the semantic representations against subjects that read specific news sites. Results demonstrate that similarities between news sites are specific to the words under consideration and that news site-specific representations successfully predict differential priming effects in lexical decision as a function of news readership. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Multisource evidence theory‐based fraud risk assessment of China's listed companies

Abstract Financial fraud has great negative impact on capital markets. It misleads investors and the government and will hurt the development of the capital market in China. To detect corporate fraud, an artificial intelligence‐based method is proposed to evaluate the fraud risk. A new model for assessing the fraud risk of listed companies in China is put forward. The proposed approach collects multisource evidence from inside and outside listed companies. The internal evidence for fraud risk assessment is gathered by a machine‐learning method. The external evidence for fraud risk assessment is obtained by a web crawler. An evidence theory‐based is employed for integrating the multisource evidence. The Fraud Risk Assessment Model based on Multisource Evidence Theory is established. The proposed model is applied to assess the fraud risk of the listed companies in China. The results indicate that the proposed model is effective at evaluating the fraud risk of listed companies, and it also has a higher precision, recall, and F‐1 measure than the traditional probability model.
Self-Propagation Graph Neural Network for Recommendation

In recommendation tasks, we model user preferences by learning node representations (i.e., user and item embeddings) based on the observed user-item interaction data, which is a bipartite graph. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> eural <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etworks ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GNN</b> s) are widely used to refine the representations by exploring the topology of the graph: embeddings of neighbors are propagated to each node to reconstruct its embeddings. However, the propagation strategy in existing GNNs is empirical and defective: (1) a substantial proportion of links are missed in the sparse observed graph, which causes ineffective and biased propagation; and (2) the propagation weights are determined by a coarse pre-defined rule, which only takes the degree of nodes into consideration. In this paper, we propose a dense and data-driven propagation mechanism for GNNs. Considering the graph we use to propagate embeddings in recommendation tasks is extremely sparse, we complement it and use the predicted graph as the new propagation tool. We learn the propagation matrix from the data, and propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> elf-propagation <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> eural <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SGNN</b> ). Since it is very space- and time-consuming to maintain a large and dense propagation matrix, we factorize it for storing and updating. In this paper, we propose three methods to complete the sparse graph and construct the propagation matrix: (1) we complete the graph based on a recommendation model; (2) we measure the node distance based on spectral clustering; (3) we predict missing links of the graph based on predictive embeddings. In SGNN, the embeddings can be propagated to not only the observed neighbors, but also the potential yet unobserved neighbors, and the propagation weights are learned based on the connection strength. Comprehensive experiments on three real-world datasets demonstrate the effectiveness and efficiency of our proposed model: SGNN outperforms recent state-of-the-art GNNs significantly. Codes are available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Wenhui-Yu/LCFN</uri> .