Reviewerly/exHarmony huggingface dataset:
Wheeled Robot Visual Odometer Based on Two-dimensional Iterative Closest Point Algorithm

Abstract According to the two-dimensional motion characteristics of planar motion wheeled robot, the visual odometer was dimensionally reduced in this study. In the feature point matching part of visual odometer, the contour constraint was used to filter out the mismatched feature point pairs (abbreviated as FPP). This method could also filter out the matched FPP, and the feature of FPP was correct color image matches, however, their depth image error was large. This offered higher quality matched FPP for the subsequent interframe motion estimation. Dimension reduction was performed in the interframe motion estimation part, and the two-dimensional Iterative Closest Point (ICP) algorithm was used for camera motion estimation. The experiments indicated that the proposed algorithm effectively improved the computational speed and precision of planar motion wheeled robot visual odometer. This research indicates that the dimension reduction processing of ICP algorithm can effectively improve the operation speed and calculation accuracy of planar motion wheeled robot visual odometry, which provides a good reference and data support for the subsequent research of wheeled robot visual odometry in the future.
SASHA: Hyperparameter Optimization by Simulated Annealing and Successive Halving

Successive halving (SHA) has become popular for hyperparameter optimization since it often yields good results while consuming a considerably lower training budget than traditional brute-force algorithms. Nevertheless, SHA is known to suffer from the crossing curves problem. A configuration that performs poorly at the start may perform very well with more budget in later rounds, causing its loss curve to cross that of competing configurations. When this happens, there is a risk that promising configurations are discarded prematurely. In this paper, we propose a new variant of SHA which we name SASHA, that combines Simulated Annealing (SA) with SHA. SASHA discards configurations stochastically based on their performance relative to the best-performing configuration. This property allows the algorithm to make more informed decisions about the configurations to be discarded each round. We study the behavior of SASHA when optimizing a simple test function and a logistic regression model. Our results indicate that SASHA can effectively deal with the crossing curves problem.
Encoding and decoding‐based normalizing flows for image anomaly localization

Abstract The authors propose encoding and decoding network‐based normalizing flows (EaD‐Flow) for the detection and localization of anomalies in an image. Multiple networks of the EaD‐Flow estimate probability densities of tenor image. The anomaly localization model based on multiple networks shows superior anomaly detection and localization performance.
An Analysis of English Textbook for Fourth Grade Students of Elementary School

The purpose of this study was to determine if the English textbook My Next Words for the fourth grade was acceptable for English Language Teaching based on the Badan Standar Nasional Pendidikan (BSNP). The study employed descriptive qualitative research with document or content analysis. The research material was derived from My Next Words, an English textbook for Grade 4 elementary school students in Indonesia. The textbook was analyzed in terms of materials, language features, and graphic under the list of content requested by BSNP 2022. According to the findings, the My Next Words textbook for a fourth grade was categorized as good and acceptable according to the BSNP 2022 criterion. Grammar and spelling in the textbook, on the other hand, require greater attention. Equally important, various cultures must be covered in the textbook.
On Improving the Accuracy of Internet Infrastructure Mapping

This study identifies a method to create fine-grained multilayer maps of the Internet’s structure, which are currently lacking. We begin with an investigation of current techniques for geolocating hosts using passive, active, and hybrid methods. This is followed by a survey of the fundamental problems that IP geolocation techniques must address. The survey points to the obvious difficulties in using Delay-Distance models and suggests that the use of Return Trip Times can lead to highly misleading results. We therefore develop a new procedure that combines state-of-the-art methods to avoid many of the fundamental problems in Internet topology mapping, whilst creating finer-grained internet maps than those currently available. This procedure is tested on the UK infrastructure by conducting a series of tests using distributed measurement points provided by the RIPE Atlas platform. Our results show that we can accurately geolocate routers between two endpoints to create a fine-grained map of the internet infrastructure involved in our measurements. Researchers have long recognized the scarcity of ground truth datasets where IP geolocation is a concern. As a byproduct of our new method reported in this paper, we create a validation dataset that maps hundreds of IP addresses to geo-coordinate landmarks or vantage points, which is highly desirable for IP geolocation research. Finally, we discuss some limitations of this method, and we summarise the next steps toward accurate and complete Internet infrastructure maps.
Image-based robot navigation with task achievability

Image-based robot action planning is becoming an active area of research owing to recent advances in deep learning. To evaluate and execute robot actions, recently proposed approaches require the estimation of the optimal cost-minimizing path, such as the shortest distance or time, between two states. To estimate the cost, parametric models consisting of deep neural networks are widely used. However, such parametric models require large amounts of correctly labeled data to accurately estimate the cost. In real robotic tasks, collecting such data is not always feasible, and the robot itself may require collecting it. In this study, we empirically show that when a model is trained with data autonomously collected by a robot, the estimation of such parametric models could be inaccurate to perform a task. Specifically, the higher the maximum predicted distance, the more inaccurate the estimation, and the robot fails navigating in the environment. To overcome this issue, we propose an alternative metric, "task achievability" (TA), which is defined as the probability that a robot will reach a goal state within a specified number of timesteps. Compared to the training of optimal cost estimator, TA can use both optimal and non-optimal trajectories in the training dataset to train, which leads to a stable estimation. We demonstrate the effectiveness of TA through robot navigation experiments in an environment resembling a real living room. We show that TA-based navigation succeeds in navigating a robot to different target positions, even when conventional cost estimator-based navigation fails.
The effectiveness of blockchain technology in preventing financial cybercrime

The primary objective of this research is to study the effectiveness of blockchain technology in preventing financial cybercrime. In this research, the researcher intends to know the effectiveness of blockchain technology in preventing financial cybercrime, the white-collar crime which is growing drastically globally. The researcher uses the primary method to collect the data. In this study, four different variables that influence financial cybercrime significantly are immutability, smart contract, distributed ledger technology and consensus algorithm. The data was collected from the targeted respondents which are accountants, IT experts and human resources. Statistical Package of the Social Sciences (SPSS) is being utilized to evaluate the relationship between the four variables which able to influence financial cybercrime. A total of 70 survey questionnaires were delivered to the targeted respondents via a convenience sampling strategy. Responses from 70 participants were entered into SPSS one by one to generate descriptive and inferential statistics. Financial cybercrime is positively correlated with immutability, smart contract, distributed ledger technology and consensus algorithms. As a result, the analysis of the collected data for this research rejects the null hypothesis, while supporting the alternative hypothesis. The conclusion that can derive from this research is that users and organizations should be aware of the financial cybercrime risks that take place around them and the importance to have vital tools that are not vulnerable to malicious attacks. This research creates awareness for users and companies on the usage of blockchain to prevent financial cybercrime.
Counting and Tracking People to Avoid from Crowded in a Restaurant Using mmWave Radar

One key to implementing the smart city is letting the smart space know where and how many people are. The visual method is a scheme to recognize people with high accuracy, but concerns arise regarding potential privacy leakage and user nonacceptance. Besides, being functional in a limited environment in an emergency should also be considered. We propose a real-time people counting and tracking system based on a millimeter wave radar (mmWave) as an alternative to the optical solutions in a restaurant. The proposed method consists of four main procedures. First, capture the point cloud of obstacles and generate them using a low-cost, commercial off-the-shelf (COTS) mmWave radar. Next, cluster the individual point with similar properties. Then the same people in sequential frames would be associated with the tracking algorithm. Finally, the estimated people would be counted, tracked, and shown in the next frame. The experiment results show that our proposed system provided a median position error of 0.17 m and counting accuracy of 83.5% for ten insiders in various scenarios in an actual restaurant environment. In addition, the real-time estimation and visualization of people's numbers and positions show a potential capability to help prevent crowds during the pandemic of Covid-19 and analyze customer visitation patterns for efficient management and target marketing.
Object Detection from Point Clouds for Driverless Vehicles

Abstract Point clouds offer the advantage of providing direct access to 3D data. So, utilizing solely point clouds, we present an object detection technique for driverless vehicles in this study. In this method, a graph is first constructed using the k-nearest neighbour (KNN) method, and then a graph neural network module is proposed for local information extraction. Then, depending on the coordinates of points, we utilize a pillar-based projection approach to project the locally informative feature into bird’s-eye-view (BEV). After that, residual-based networks with attention mechanisms are used for the BEV features processing. The attention system is capable of assigning appropriate weights to several nearby points, hence improving detection performance. The proposed work achieves 58.49 percent and 67.90 percent mAP (mean Average Precision) for 3D Object detection and BEV detection, respectively, on the KITTI 3D object detection benchmark. We also tested the proposed method on our driverless vehicle in our campus, and we compared it to the prior method we utilized. The experimental results reveal that the proposed method outperforms the existing methods (about 2% higher than PointPillars in BEV detection) and gives more accurate information.
Alternative Ruleset Discovery to Support Black-Box Model Predictions

The increasing attention to the interpretability of machine learning models has led to the development of methods to explain the behavior of black-box models in a post-hoc manner. However, such post-hoc approaches generate a new explanation for every new input, and these explanations cannot be checked by humans in advance. A method that selects decision rules from a finite ruleset as explanation for neural networks has been proposed, but it cannot be used for other models. In this paper, we propose a model-agnostic explanation method to find a pre-verifiable finite ruleset from which a decision rule is selected to support every prediction made by a given black-box model. First, we define an explanation model that selects the rule, from a ruleset, that gives the closest prediction; this rule works as an alternative explanation or supportive evidence for the prediction of a black-box model. The ruleset should have high coverage to give close predictions for future inputs, but it should also be small enough to be checkable by humans in advance. However, minimizing the ruleset while keeping high coverage leads to a computationally hard combinatorial problem. Hence, we show that this problem can be reduced to a weighted MaxSAT problem composed only of Horn clauses, which can be efficiently solved with modern solvers. Experimental results showed that our method found small rulesets such that the rules selected from them can achieve higher accuracy for structured data as compared to the existing method using rulesets of almost the same size. We also experimentally compared the proposed method with two purely rule-based models, CORELS and defragTrees. Furthermore, we examine rulesets constructed for real datasets and discuss the characteristics of the proposed method from different viewpoints including interpretability, limitation, and possible use cases.
A Study of Malaria in Chandigarh

In 2016, the Indian government came out with the framework for malaria elimination in India covering 2016-2030. 4This utilized World Health Organization (WHO) global technical strategy for malaria covering 2016-2030, which came out in 2015 and was recently updated in 2021. 5 Sir, Chandigarh lies in the northern part of India.It is bounded by Punjab in the north, south, and west and Haryana in the east.An attempt was made to study the status of malaria in Chandigarh.The study design included an analysis of the annual reports of the National Center for Vector Borne Diseases Control (NCVBDC) and a study of the website of the National Health Mission, Chandigarh, under which the Chandigarh NCVBDC functions.According to the most recent data available on the NCVBDC website (data for 2018), the application programming interface (API) for Chandigarh was 0.04. 1 The API for 2017 was 0.10. 2 Thus, it is seen that the API in 2018 was less than half of that seen in 2017.An internet search revealed the following information on malaria cases in Chandigarh from 2019 to 2021: 2019-22 cases, 2020-seven cases, and 2021-four cases (up to October). 3 Beginning in 2017, there has been a decline in the incidence of malaria in Chandigarh.
Blockchain-enhanced certificateless signature scheme in the standard model

The Internet of Things (IoT), driven by wireless communication and other technologies, is gradually entering our lives and promoting the transformation of society from "informatization" to "intelligence". Certificateless signature (CLS) eliminates the characteristic of certificate management, making it an effective method for verifying large-scale data in the IoT environment. Nevertheless, hash functions are regarded as ideal random oracles in the security proofs of most CLS schemes, which cannot guarantee the security of CLS schemes in reality. In response to this problem, Shim devised a CLS scheme without random oracles in the standard model and declared it to be provably secure. Unfortunately, in this paper, we cryptanalyze Shim's CLS scheme and demonstrate that it is not resistant to public key replacement attacks from a Type Ⅰ attacker. Furthermore, to further improve the security of the Shim CLS scheme and avoid the single-point failure of the KGC and the signature forgery initiated, we propose a blockchain-based CLS scheme without a random oracle. Finally, we evaluate the comprehensive performance, and while maintaining the computational and communication performance of the Shim scheme, we resist both Type Ⅰ and Type Ⅱ attackers, as well as signature forgery initiated against public parameters.
Objective Weight Interval Estimation Using Adversarial Inverse Reinforcement Learning

Several real-world problems are modeled as multi-objective sequential decision-making problems with multiple competing objectives, and multi-objective reinforcement learning (MORL) has garnered attention as a solution to this problem. One of the challenges in obtaining the desired policy using MORL is that the priorities (hereafter, weights) for each objective must be designed in advance to scalarize the reward vector. Determining weights through trial-and-error burdens system designers, and methods to estimate weights are needed. The existing methods use inverse reinforcement learning (IRL), which is not scalable because it requires reinforcement learning several times until an optimal policy is obtained. This study proposes a weight interval estimation (WInter) method using adversarial IRL (AIRL). AIRL is a scalable framework that reduces the computational complexity of IRL by simultaneously estimating rewards and policies. WInter estimates the weight interval using the expert neighborhoods obtained during AIRL training. We successfully estimated the weight interval through experiments in a benchmark environment for multi-objective sequential decision-making problems in a continuous state space while reducing computational complexity compared to the existing methods.
Designing network visualizations for genetic literary criticism

In this paper we present the outcomes of a research aimed at designing a new visual model for the analysis and presentation of data of genetic criticism carried out in collaboration between researchers in information design and literary scholars of the project [name of the project redacted for blind review]. The design process involved three moments: gathering of information for the definition of design requirements, prototyping the networks and conducting preliminarily evaluations, and producing and evaluating ten network visualizations. The presented process is rich in insights about the collaboration between design researchers and scholars involved in digital humanities.
Task prediction on tablet based deep learning method

The ability of smartphones and other mobile technology to improve cognitive abilities has long been acknowledged by all. This model includes pre-processing, enhanced higher-order feature extraction, and prediction stages. At first, the collected data from different age groups is pre-processed using a data cleaning approach. The feature extraction stage extracts the crucial features from the pre-processed data, including enhanced higher-order statistical features. The final prediction regarding the automatic prediction of variations in pupil diameter of the left eye, right eye, and reading ability (count of characters read/second) is made using three mathematical models: optimized Deep Convolutional Neural Network 1 (DCNN1), optimized DCNN2, and optimized DCNN3. Furthermore, the weight functions of the three mathematical models are fine-tuned to increase prediction accuracy using a hybrid optimization model called Arithmetically Updated Poor Rich Optimization (AUPRO). The comparative assessment is undergone to verify the efficiency of the projected model in terms of various measures.
Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift

Database and data structure research can improve machine learning performance in many ways. One way is to design better algorithms on data structures. This paper combines the use of incremental computation as well as sequential and probabilistic filtering to enable “forgetful” tree-based learning algorithms to cope with streaming data that suffers from concept drift. (Concept drift occurs when the functional mapping from input to classification changes over time). The forgetful algorithms described in this paper achieve high performance while maintaining high quality predictions on streaming data. Specifically, the algorithms are up to 24 times faster than state-of-the-art incremental algorithms with, at most, a 2% loss of accuracy, or are at least twice faster without any loss of accuracy. This makes such structures suitable for high volume streaming applications.
Using Decision Trees to Predict Critical Reading Performance.

In Colombia, all undergraduate students, regardless of the professional training program they take, must complete the general competencies sections of the Saber Pro exam that include Critical Reading, Quantitative Reasoning, Citizen Competencies, Written Communication, and English. This paper presents the application of the classification technique based on decision trees in the prediction of the performance in the Critical Reading section presented by the students of the Pontificia Universidad Javeriana Cali in the years 2017 and 2018. The CRISP methodology was used. From the socioeconomic, academic and institutional data stored in the ICFES databases, a data repository was built, cleaned and transformed. A mineable view composed of 2052 records and 17 attributes was obtained. The J48 algorithm of the Weka tool was used to build the decision tree. The score obtained in the Critical Reading section of the Saber Pro exam was taken as a class. According to the results obtained, the Philosophy, Applied Mathematics, and Medicine programs stood out for having the best performance in this test. Among the predictive variables associated with performance in the Critical Reading skill are the faculty, the age group and the student's transportation index, as three important variables related to the good or low academic performance of the students of the Universidad Javeriana Cali. The knowledge generated in this research is constituted in quality information to support the decision-making process of the university directives in order to improve the quality of the higher education offered in this institution.
Pseudo Features-Guided Self-Training for Domain Adaptive Semantic Segmentation of Satellite Images

Semantic segmentation is a fundamental and crucial task that is of great importance to real-world satellite image-based applications. Yet a widely acknowledged issue that occurs when applying the semantic segmentation models to unseen scenery is that the model will perform much poorer than when it was applied to scenery similar to the training data. This phenomenon is usually termed as the domain shift problem. To tackle it, this article presents a self-training-based unsupervised domain adaptation (UDA) method. Different from the previous self-training approaches which focus on rectifying and improving the quality of the pseudo labels, we instead seek to exploit feature-level relation among neighboring pixels to structure and regularize the prediction of the adapted model. Based on the assumption that spatial topological relation is maintained despite the impact of the domain shift, we propose a novel self-training mechanism to perform DA by exploiting local relation in the feature space spanned by the teacher model, from which the pseudo labels are generated. Quantitative experiments on four different public benchmarks demonstrate that the proposed method can outperform the other UDA methods. Besides, analytical experiments also intuitively verify the proposed assumption. Codes will be publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhu-xlab/PFST</uri> .