Reviewerly/exHarmony huggingface dataset:
Accuracy of Computer Vision Hough Line Transform for Water Level Measurement

Abstract Floods are natural disasters that often occur in countries with high rainfall, such as Indonesia. Floods occur due to sudden changes in water level; therefore, the dam cannot withstand the increase in water causing water to overflow into the riverbank area. As a result of this issue, a system was created that can be used to provide information about river water levels. This system relies on computer vision to program the camera to monitor the water level through image processing. Image processing allows the camera to process the image of the water surface detected at the ditch’s edge. The edge detection result is used as a water level detector using the Hough line transform, which generates a line to indicate the water level. The height detected by image processing differs from the actual height on the detected surface. The minimum and maximum error reading for both methods recorded in this experiment is 0,8 % and 16,67 %, respectively
Explore Gap between 3D DNN and Human Vision Utilizing Fooling Point Cloud Generated by MEHHO

Deep neural network (DNN) has replaced humans to make decisions in many security-critical senses such as face recognition and automatic drive. Essentially, researchers try to teach DNN to simulate human behavior. However, many evidences show that there is a huge gap between humans and DNN, which has raised lots of security concern. Adversarial sample is a common way to show the gap between DNN and humans in recognizing objects with similar appearance. However, we argue that the difference is not limited to adversarial samples. Hence, this paper explores such differences in a new way by generating fooling samples in 3D point cloud domain. Specifically, the fooling point cloud is hardly recognized by human vision but is classified to the target class by the victim 3D point cloud DNN (3D DNN) with more than <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn mathvariant="bold">99.99</mn> <mo>%</mo> </math> confidence. Furthermore, to search for the optimal fooling point cloud, a new evolutionary algorithm named Multielites Harris Hawk Optimization (MEHHO) with enhanced exploitation ability is designed. On one hand, our experiments demonstrate that: (1) 3D DNN tends to learn high-level features of one object; (2) 3D DNN that makes decisions relying on more points is more robust; and (3) the gap is hardly learned by 3D DNN. On the other hand, the comparison experiments show that the designed MEHHO outperforms the SOTA evolutionary algorithms w.r.t. statistics and convergence results.
The Impact of Embodiment on Training Effectiveness

Due to the COVID-19 pandemic and compulsory social distancing, researchers in educational fields started to investigate alternatives to face-to-face (F2F) training methods with greater focus, such as video conferencing (VC) and virtual reality (VR) applications. This study investigated the differences between VC, VR and F2F training conditions by evaluating the level of body ownership and agency perceived by trainees. An electrical circuit repair task and multiple surveys were used to gather data from 106 participants in the form of four dependent variables: a circuit knowledge test, task completion rate, number of the subtasks completed by failing participants, and test phase duration. The study included two visits by each participant to measure knowledge retention while there were no training and surveys in Visit 2. Results showed significantly higher circuit learning and knowledge retention scores in F2F and VR conditions than in VC. Also, regarding the retention of knowledge, participants had significantly better knowledge retention in Visit 1 than Visit 2. The authors hope the results of this study enable training developers to enhance the learning process in computer mediated communications.
Unsupervised Image Anomaly Detection and Localization in Industry Based on Self-Updated Memory and Center Clustering

Defect detection of industrial products often uses computer vision methods. Detecting anomalies in the image can reflect the defect of the product. In order to adapt to the scene of less defect samples and unclear defect classification standards in industrial production and improve the accuracy and robustness of detection, this paper proposes a new unsupervised anomaly detection and Localization framework based on self-updated memory and center clustering (SMCC). Distinct from previous works, it uses a pre-trained model to extract image features, and then uses a Gaussian mixture model to cluster and obtain cluster centers, so that normal sample features are compactly distributed around the cluster centers, thereby better distinguishing normal and abnormal sample features. The advantage of the self-updated memory bank is to reduce the use of memory and adjust the parameters of the pre-trained network to make it more suitable for the distribution of the current dataset. Our experiments on the MVTec AD and other datasets show the effectiveness of SMCC for anomaly detection and localization.
Applications of Deep Learning in Healthcare: A Systematic Analysis

Deep learning (DL) is a subfield of artificial intelligence (AI) that deals with the recognition of patterns. It learns from the input provided to it to predict an output according to the features it evaluates. With the extensive increase in unstructured data in the past few years, the ability to train machines to predict outcomes became much more difficult but the development of artificial neural networks (ANNs) and DL techniques changed that. One of the biggest advancements made with DL is in the field of healthcare. The objective of this research is to provide a comprehensive analysis of the vast applications of DL techniques used in the healthcare system, specifically in the domains of drug discovery, medical imaging, and electronic health records (EHRs). Due to the past epidemics and the current situation of the ongoing pandemic disease, i.e., COVID-19, the application of AI, ML, and DL in this field has become even more critical. Such work has become even more significant, and these techniques can help make timely predictions to combat the situation. The result showed a lot of research is ongoing to continuously tackle the limitations and improve upon the advantages. Many important advancements have been made in the field and will continue to grow and make our quality of life more efficient, cost-effective, and effortless.
An IoT-Based Approach for Visibility Enhancement and Fog Detection

Nowadays, many camera-based advanced driver assistance systems have been created in recent years to assist drivers and assure their safety in a variety of driving situations. When driving in foggy circumstances, one of the issues drivers confront is decreased scene visibility and lower contrast. Aside from the visibility issues, the driver needs also to make a speed decision when driving. The major facts of fog are loss of contrast and color fading. Rain and snow are exceedingly disruptive to drivers, and glare from the sun or other road users can be quite dangerous, even if only for a short time. This paper aims to provide a novel approach that will let the users know about foggy weather conditions. The proposed system uses smart fog sensors by employing the Internet of Things (IoT). IoT is an emerging field that can be used for the intelligent transport system that can be used in vehicles due to zero visibility in fog. This paper highlights the approaches and systems that have been discovered and deemed important for estimating or even improving visibility in bad weather. This paper elaborates that the proposed technique can approximate and remove the corresponding fog functions for better visibility and safety.
An Improved Arithmetic Optimization Algorithm with Differential Evolution and Chaotic Local Search

These days the notion to hybridize metaheuristics has become popular among researchers due to the advantages it presents to tackle complex and difficult optimization problems. This method makes it easy to combine the strength of two or more algorithms to better traverse a search space. The arithmetic optimization algorithm (AOA) is a recently developed metaheuristic that has received enormous attention among researchers due to its simple and robust nature. The algorithm applies selective pressure using the arithmetic operators (+, −, ×, ÷) to explore and exploit a search space. This action is highly desirable when the process has identified the optimal region, but could be detrimental in situations where the algorithm needs to visit new and diverse regions to escape getting trapped. To address this problem, this study combines AOA and a modified differential evolution (DE) mutation strategy to help solutions jump out of local optimum and prevent premature convergence. Furthermore, we added a chaotic local search operator into AOADE to help improve the quality of the best solution at the end of each iteration. Experimental results on the IEEE CEC 2014 benchmark functions indicate that our proposed method obtained better results in at least 20 out of the 30 benchmark functions tested as compared to other state of the art algorithms.
Practical Usage of Radical Isogenies for CSIDH

Recently, a radical isogeny was proposed to boost commutative supersingular isogeny Diffie–Hellman (CSIDH) implementation. Radical isogenies reduce the generation of a kernel of a small prime order when implementing CSIDH. However, when the size of the base field increases, field exponentiation, a core component of computing radical isogenies, becomes more computationally intensive. As the size of the field inevitably grows to resist a quantum attack, so it is necessary to discuss the practical utilization of the radical CSIDH. This paper presents an optimized implementation of radical isogenies and analyzes its ideal use in CSIDH-based cryptography with a review of quantum analysis. We tailored the formula for transforming Montgomery curves into the Tate normal form and further optimized the radical 2-isogeny formula and projective versions of the radical 5- and 7-isogenies. Except for CSIDH-512, using only the radical 2-isogeny for all parameters improves performance by 6% to 10%.
Financial outreach, financial innovation, and sustainable development in Africa

There has been a call on policy makers in the African continent to formulate and implement initiatives that help to realise some of the Sustainable Development Goals (SDGs), due to the low performance of the continent in terms of meeting the targets of the SDGs. Because of this, the study sought to investigate how banks' financial outreach and intermediation contribute to sustainable development in the continent. Information was collected on 34 African economies for a period of 11 years spanning from 2010 to 2020. The study employed the two-step system generalised method of moments technique to estimate the findings. It was discovered that financial outreach has a significant positive and negative relationship with sustainable development, depending on the indicator used to measure outreach. On various dimensions, financial outreach had a negative influence on carbon dioxide emissions, a positive impact on economic sustainability, and an inverse relationship with social sustainability. It was also revealed that financial innovation has a significant negative link with sustainable development in Africa. Additionally, the findings revealed that both financial outreach and innovation serve as moderating variables in the finance/development nexus. The study recommends that governments and policy makers in various African countries work together with financial service providers to ensure fair, flexible, and alluring interest rates on loans to the underprivileged, disadvantaged ones in society, and vulnerable businesses to smooth their consumption and boost their businesses.
RRNMF-MAGL: Robust regularization non-negative matrix factorization with multi-constraint adaptive graph learning for dimensionality reduction

In this paper, a new unsupervised dimensionality reduction method named robust regularization non-negative matrix factorization with multi-constraint adaptive graph learning (RRNMF-MAGL) is developed. Compared with the existing methods, RRNMF-MAGL can extract robust low-dimensional features and adaptively learn a manifold structure to well reflect the data distribution. Specifically, to alleviate the influence of outliers and noises, a robust non-negative matrix factorization (RNMF) approach with L21-norm loss function is first constructed. Then, a multi-constraint adaptive graph learning (MAGL) model is designed based on low-dimensional features to reduce the computational complexity and avoid the adverse influence of redundant information. Moreover, multiple constraints (i.e., sparsity and locality) are incorporated into graph learning for enhancing the discrimination ability of graph structure. Next, to make the learned low-dimensional features well maintain the local geometric structure information of data, a graph Laplacian regularization (GLR) is designed. Finally, an iterative update strategy is proposed to optimize RRNMF-MAGL and its convergence is verified by numerical experiments. The superior effectiveness and robustness of RRNMF-MAGL is demonstrated by extensive experiments on eight publicly available datasets.
Combining raw data and engineered features for optimizing encrypted and compressed internet of things traffic classification

The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. The increased reliance on IoT raised serious information security concerns. As IoT traffic is often compressed to optimize resource consumption, sending compressed data without encryption is a significant security risk for violating confidentiality, integrity, and privacy, as the data can be decompressed, read, and modified. The randomization generated by data compression can complicate classifying compressed data from encrypted data. Encrypted and compressed traffic classification is crucial for the security and forensic analysis of IoT implementations. Existing encrypted and compressed data classification approaches suffer from various limitations. This work proposes two Deep Learning (DL) systems to enhance traffic classification with an emphasis on reducing the possibility of falsely classifying compressed traffic as encrypted. The first approach is Engineered Features Classification (EFC), which uses a set of statistical tests. The second approach is Raw Data and Engineered Features Classification (RDEFC), which combines raw data and statistical tests to improve the classification of encrypted and compressed traffic. Our work also addresses the complexity of classifying encrypted samples of compressed files, such as encrypted JPG images. A large dataset is built in this work with different file types, which include TXT, HTML, WAV, PDF, and JPG. Our evaluation results show high performance for the EFC system with classification 80.94% accuracy. The RDEFC system has a significant improvement over the former with 90.55% classification accuracy. Our approaches outperform systems reported in the literature with similar configurations.
The Minimalist Foundation and Bishop’s Constructive Mathematics

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Fast computation of cluster validity measures for bregman divergences and benefits

Partitional clustering is one of the most relevant unsupervised learning and pattern recognition techniques. Unfortunately, one of the main drawbacks of these methodologies refer to the fact that the number of clusters is generally assumed to be known beforehand and automating its selection is not straightforward. On the same token, internal validity measures, such as the Silhouette index, Davies-Bouldin and Caliski-Harabasz measures have emerged as the standard techniques to be used when comparing the goodness of clustering results obtained via different clustering methods. These measures take into consideration both the inter and intra-cluster simmilarities and can be adapted to different metrics. Unfortunately, their used has been hindered due to their large computational complexities, which are commonly quadratic with respect to the number of instances of the data set. In this work, we show that the time complexity of computing the most popular internal validity measures can be utterly reduced by making used of the within-cluster errors and different properties of the Bregman divergences. This contribution ultimately allows us to massively speed-up the selection of an adequate number of clusters for a given data set as verified with extensive empirical comparisons.
Benchmark of Deep Learning Visual and Far-Infrared Videos Toward Weather-tolerant Pedestrian Traffic Monitoring

Sidewalks should be developed and integrated into city planning for economic growth and livability. And it is necessary to obtain data on pedestrians. One type of pedestrian data, pedestrian counting, commonly uses visible and far-infrared cameras to detect and track people. However, it is not clear in which situations the number of pedestrians can be efficiently estimated from each video. In this study, we use visible and far-infrared videos captured on sidewalks to estimate the number of pedestrians. And we investigate the effects of various times and weather on the estimation accuracy. Experimental results showed that the head count accuracy decreased when using visible video at night and in the rain. When using far-infrared video, the accuracy of head count decreased in the range of 23°C-30°C of ambient temperature. This study revealed that in order to achieve a highly accurate head count throughout the day, it is necessary to change the video used depending on the time and ambient temperature or to use both types of video.
Uncovering Resilient Actions of Robotic Technology with Data Interpretation Trajectories Using Knowledge Representation Procedures

This article highlights the importance of learning models which prevent the resilient attack of robotic technology with a subset of trajectories. Many complement models are introduced in the field of path planning robots without any knowledge of representation procedures, so robotic data are subject to different attacks from several users. During such attacks, the data will be misplaced and commands specified to robots will be disorganized so a new training data set has to be incorporated which is a difficult task. Therefore, to prevent probability of data failure time-dependent binary probability prototypes are introduced with low training data. Furthermore, a regularized boosting procedure (RBP) has been applied with different weights to switch multiple robots with discrete knowledge representation. Then a high space block is incorporated for maximizing coverage areas during loss functions and this is implicit as an innovative technique as compared with existing procedures. To validate the effectiveness of proposed learning techniques in robots, four scenarios are considered which include accuracy and success rate of detection. Subsequently, the outcomes prove that the robotic path with learning models are highly effective for an average percentile of 86% as compared to conventional techniques.
TMG: A topology-based motion generalization method with spatial relationship preservation

As an important requirement, human-friendly motion in human–robot interaction (HRI) is increasingly attracting attention. In many scenarios, the ability to generalize a similar motion from demonstration is essential for a robot, and the similarity is generally captured by the spatial relationship between the different joints of the robot. Though a lot of investigation for motion generalization has been conducted and good progress achieved, they share limitations in leaving the relationship out of consideration and being difficult to apply the generalization between different robots. In this paper, we propose a novel topology-based motion generalization (TMG) method that abstracts the motion generalization problem to a mesh deformation optimization, and the spatial relationship between different parts of the robot is captured with a topology-based representation. Instead of only taking into account a single joint position, the relationship semantic with Laplacian coordinates is modeled, and the motion generalization from demonstration to reproduction is realized by preserving the semantic as a Laplacian deformation, and even the robot or target position is changed. Furthermore, motion generalization between single or multiple different robots can be achieved with spatial relationship preservation and transfer. Our experimental results show that the reproduction based on topology-based representation outperforms the mapping methods by training with end-effector pose or joint angles, and ensures robust motion with spatial relationship preservation.
Self-paced deep clustering with learning loss

Deep clustering has become a popular technique in various fields due to its superior performance over conventional clustering. However, it can be challenging to classify data located near the decision boundary, and difficult or noisy samples can confuse or mislead the training process of deep neural networks, ultimately impacting clustering performance. To address this issue, we propose a novel self-paced deep clustering method that gradually increases the number of samples input into the network from easy to difficult. Our approach involves attaching a loss prediction module to the convolutional neural network to judge the difficulty of samples. The module is robust as it relies on the input contents, not statistical estimates of uncertainty from outputs. Moreover, this module selects the most informative data for the training model, thus enabling the network to converge stably and reach a good optimal solution. Finally, we consider the reconstruction error, clustering loss, and loss prediction error to construct the loss of the model. Experimental results on four image datasets show that our method outperforms state-of-the-art works. The main code can be found at https://github.com/clustering106/SDCLL.