Cognitive Computation and Systems

In recent years, human hand‐based robotic hands or dexterous hands have gained attention due to their enormous capabilities of handling soft materials compared to traditional grippers. Back in the earlier days, the development of a hand model close to that of a human was an impossible task but with the advancements made in technology, dexterous hands with three, four or five‐fingered robotic hands have been developed to mimic human hand nature. However, human‐like manipulation of dexterous hands to this date remains a challenge. Thus, this review focuses on (a) the history and motivation behind the development of dexterous hands, (b) a brief overview of the available multi‐fingered hands, and (c) learning‐based methods such as traditional and data‐driven learning methods for manipulating dexterous hands. Additionally, it discusses the challenges faced in terms of the manipulation of multi‐fingered or dexterous hands.

Suhas Kadalagere Sampath

Ning Wang

Hao Wu

Chenguang Yang

Cognitive Comp and Systems

Review on human‐like robot manipulation using dexterous hands

人形机器人使用灵巧手进行操作的综述

Abstract Brain networks provided powerful tools for the analysis and diagnosis of epilepsy. This paper performed a pairwise comparative analysis on the brain networks of Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS): spike group (spike), non‐spike group (non‐spike), and control group (control). In this study, fragments with and without interictal spikes in electroencephalograms of 13 BECTS children during non‐rapid eye movement sleep stage I (NREMI) were selected to construct dynamic brain function networks to explore the functional connectivity (FC). Graph theory and statistical analysis were exploited to investigate changes in FC across different brain regions in different frequency bands. From this study, we can draw the following conclusions: (1) Both spike and non‐spike have lower energy in each brain region on the γ band. (2) With the increase of the frequency band, the FC strength of spike, non‐spike and control groups are all weakened. (3) Spikes are correlated with brain network efficiency and the small‐world property. (4) Spikes increase the FC of temporal, parietal and occipital regions except in the γ band and the absence of spikes weakens the FC of the entire brain region.

Zhixing Hong

Dinghan Hu

Runze Zheng

Tiejia Jiang

Feng Gao

Jiajia Fang

Jiuwen Cao

Brain network analysis of benign childhood epilepsy with centrotemporal spikes: With versus without interictal spikes

良性儿童癫痫伴中央颞区棘波的脑网络分析：有无间歇期棘波的比较

Abstract Garbage management is exceptionally critical and poses enormous environmental challenges. It has always been a vital issue in municipal corporations. However, municipal agencies have developed and used garbage management systems. Garbage forecasting still plays a crucial role in the management system and helps improve or create a garbage management system. This research examines the information from 212 cities to suggest a helpful regression model for garbage forecasting and control. To establish a connection between the variables, the descriptive study employs statistical techniques to learn about the composition of data collected from municipal corporations and conduct correlation analysis. Population and garbage depend highly on one another, as evidenced by their correlation coefficient of 0.922,144. The primary research is used to build an alternate hypothesis that shows the chosen variables are highly dependent on one another. The dataset is scaled and divided into a training and testing 80:20 ratio during the pre‐processing data phase. This research aims to do a regression analysis with daily garbage production, urban area, and population as independent variables. This research initiates a variety of regression models, including multiple linear regression (MLR), artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). The MLR model's R2 value of 0.85 indicates that it has the potential to accurately forecast daily garbage production based on just two independent variables and a single dependent variable. Random Forest Regression (RFR) with (MSE: 100,078.749 &amp; MAE: 182.212) shows that it has the lowest MSE among all the models, which provides the most accurate predictions on average and the fit values of 8.85 and 316.23 obtained from the error distribution with a bin value 25. The estimated results from each model are compared to the test data values on line graphs and Taylor plots. The mean square error and the mean absolute error in the analysis and the Taylor plot show that the RFR model is best suited for predicting daily garbage production in a city. This research, therefore, provides a Random Forest model that is optimal for such challenges and is recommended for this class of problem.

Raj Kumar Sharma

Manisha Jailia

Garbage prediction using regression analysis for municipal corporations of Indian cities

印度城市市政公司垃圾预测的回归分析

Face detection is the basic step of many face analysis tasks. In practice, face detectors usually run on mobile devices with limited memory and computing resources. Therefore, it is important to keep face detectors lightweight. To this end, current methods usually focus on directly designing lightweight detectors. Nevertheless, it is not fully explored whether the resource consumption of these lightweight detectors can be further suppressed without too much sacrifice on accuracy. In this study, we propose to apply the network pruning method to the lightweight face detection network, to further reduce its parameters and floating point operations. To identify the channels of less importance, we perform network training with sparsity regularisation on channel scaling factors of each layer. Then, we remove the connections and corresponding weights with near-zero scaling factors after sparsity training. We apply the proposed pruning pipeline to a state-of-the-art face detection method, EagleEye, and get a shrunken EagleEye model, which has a reduced number of computing operations and parameters. The shrunken model achieves comparable accuracy as the unpruned model. By using the proposed method, the shrunken EagleEye achieves a 56.3% reduction of parameter size with almost no accuracy loss on the WiderFace dataset.

Nanfei Jiang

Zhexiao Xiong

Hui Tian

Xu Zhao

Xiaojie Du

Chaoyang Zhao

Jinqiao Wang

PruneFaceDet: Pruning lightweight face detection network by sparsity training

PruneFaceDet：通过稀疏训练剪枝轻量级人脸检测网络

Aiming to help improve quality of life of the visually impaired people, this paper presents a novel wearable aid in the shape of a helmet for helping them find objects in indoor scenes. An object-goal navigation system based on a wearable device is developed, which consists of four modules: object relation prior knowledge (ORPK), perception, decision and feedback. To make the aid also work well in unfamiliar environment, ORPK is used for sub-goal inference to help the user find the target goal. And a method that learns the ORPK from unlabelled images by utilising a scene graph and knowledge graph is proposed. The effectiveness of the aid is demonstrated in real world experiments.

Xuan Hou

Huailin Zhao

Chenxu Wang

Huaping Liu

Knowledge driven indoor object‐goal navigation aid for visually impaired people

知识驱动的室内目标物体导航辅助系统，用于视障人士

Abstract Health insurance plays a vital role during medical emergencies in the coverage against medical expenses. Insurance fraud is an international challenge that affects most economies worldwide. Government and private companies offer many insurance schemes. The successful implementation of numerous health insurance programs offered for the public by and large are often threatened by corruption, fraud, and numerous other data‐related issues. Further the procedure for acclaiming the insurance money is not only critical in terms of verification of claims but tedious and time consuming also. To help redress these problems, blockchain technology can be utilised as is it offers improved security, transparency, auditability, privacy, accountability along with many other advantages. The goal is to create and implement a blockchain‐based solution for efficient functioning of insurance system and to prevent such health insurance systems from going bankrupt. The authors have proposed an insurance claim model, MedBlockSure using blockchain architecture for creating interoperability between the insurer, the hospital and the insurance company. The model will aid in maintaining transparency between the insurer and the company while eliminating the requirement of middlemen or agents. The conceptual view of the proposed system using sequence and use case diagrams and data management framework and smart claim processing system is demonstrated.

Charu Krishna

Divya Kumar

Dharmender Singh Kushwaha

MedBlockSure: Blockchain‐based insurance system

MedBlockSure：基于区块链的保险系统

Abstract Low‐light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low‐light object detection is presented using state‐of‐the‐art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low‐light conditions. The ExDark dataset is a dataset that consists of adequate low‐light images, modified to simulate realistic low‐light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low‐light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low‐light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low‐light object detection, offering promising solutions for real‐world applications like nighttime surveillance and autonomous navigation in low‐light conditions, addressing the growing demand for advanced low‐light object detection.

Samier Uddin Ahammad Shovo

Md. Golam Rabbani Abir

Md. Mohsin Kabir

M. F. Mridha

Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation

利用YOLO模型提升低光目标检测：一项实证研究与性能评估

Abstract The firing of spatial cells in the entorhinal‐hippocampal structure is believed to enable the formation of a cognitive map for the environment. Inspired by the spatial cognitive mechanism of the rat's brain, the authors proposed a real‐time cognitive map construction method based on the entorhinal‐hippocampal working mechanism. Firstly, based on the physiological properties of the rat's brain, the authors constructed an entorhinal‐hippocampal CA3 neurocomputational model for path integration. Then, the transformation relationship between the cell plate and the real environment is used to solve the robot's position. Path integration inevitably generates cumulative errors, which require loop‐closure detection and pose optimisation to eliminate errors. To solve the problem that the RatSLAM algorithm is slow in pose optimisation, the authors proposed a pose optimisation method based on a multi‐layer CA1 place cell to improve the speed of pose optimisation. To validate the method, the authors designed simulation experiments, dataset experiments, and physical experiments. The experimental results showed that compared to other brain‐like SLAM algorithms, the authors’ method possesses outstanding performance in path integration accuracy and map construction speed. As a result, the authors’ method can endow mobile robots with the ability to quickly and accurately construct cognitive maps in complex and unknown environments.

Yishen Liao

Naigong Yu

A real‐time cognitive map construction method based on the entorhinal‐hippocampal working mechanism of the rat's brain

基于大鼠脑内嗅皮层-海马工作机制的实时认知地图构建方法

Abbreviation	Cognitive Comp and Systems
Journal Impact	1.30
Quartiles(Global)	ROBOTICS(Q4)

ISSN	2517-7567
h-index	10

Publication Information	Publisher: John Wiley and Sons Inc，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2019，Proportion of original research papers: 0.00%，Self Citation Rate:0.00%， Gold OA Rate: 81.32%
Journal Fees	$2200Details
Average review cycle	网友分享经验：10 Weeks

Cognitive Computation and Systems

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Share Submission Experience

Review on human‐like robot manipulation using dexterous hands

Brain network analysis of benign childhood epilepsy with centrotemporal spikes: With versus without interictal spikes

Garbage prediction using regression analysis for municipal corporations of Indian cities

PruneFaceDet: Pruning lightweight face detection network by sparsity training

Knowledge driven indoor object‐goal navigation aid for visually impaired people

Brain network analysis of benign childhood epilepsy with centrotemporal spikes: With versus without interictal spikes

Garbage prediction using regression analysis for municipal corporations of Indian cities

MedBlockSure: Blockchain‐based insurance system

Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation

A real‐time cognitive map construction method based on the entorhinal‐hippocampal working mechanism of the rat's brain