在以人为中心的计算和信息科学（HCIS）中，KIPS-CSWRG 发表了高质量的论文，涵盖了与以人为中心的计算和信息科学相关的各种理论和实际应用。已发表的论文展示了对解决应用服务的各种问题以及 HCIS 范围内的其他问题具有重要价值的结果。此外，我们预计它们将引发与我们未来生活相关的进一步相关研究和技术改进。

The journal "Human-centric Computing and Information Sciences" focuses on Human-centric Computing and Information Sciences (HCIS) and is published by KIPS-CSWRG. It publishes high-quality papers covering various theoretical and practical applications related to HCIS. The published papers demonstrate significant contributions to solving various issues in applied services and other important problems within the HCIS domain. Furthermore, we anticipate that these studies will inspire further research and technological advancements related to our future lives.

《Human-centric Computing and Information Sciences》期刊专注于以人为中心的计算和信息科学（HCIS），由KIPS-CSWRG出版。期刊发表高质量的论文，涵盖与以人为中心的计算和信息科学相关的各种理论和实际应用。已发表的论文展示了对解决应用服务的各种问题以及HCIS领域内其他重要问题的显著贡献。此外，我们期待这些研究能够引发与未来生活相关的进一步研究和技术改进。

Human-centric Computing and Information Sciences

Abstract In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches.

Giovanni Cicceri

Fabrizio De Vita

Dario Bruneo

Giovanni Merlino

Antonio Puliafito

A deep learning approach for pressure ulcer prevention using wearable computing

一种利用可穿戴计算的压疮预防深度学习方法

Abstract Low-rate denial of service (LDoS) attacks send attacking bursts intermittently to the network which can severely degrade the victim system’s Quality of Service (QoS). The low-rate nature of such attacks complicates attack detection. LDoS attacks repeatedly trigger the congestion control mechanism, which can make TCP traffic extremely unstable. This paper investigates the network traffic’ characteristics, in which variance and entropy are used to evaluate the TCP traffic’s characteristics, and the ratio of UDP traffic to TCP traffic (UTR) is also analyzed. Thus, a detection method combining two-step cluster analysis and UTR analysis is proposed. Through two-step cluster analysis which is one of the machine learning algorithms, network traffic is divided into multiple clusters and then clusters subjected to LDoS attacks are determined using UTR analysis. NS2 simulation platform and test-bed network environment aim to evaluate the detection approach’s performance. To better assess the effectiveness of the method, public dataset WIDE is also utilized. Experimental results with a good performance prove that the proposed detection approach can accurately detect LDoS attacks.

Dan Tang

Rui Dai

Liu Tang

Xiong Li

Low-rate DoS attack detection based on two-step cluster analysis and UTR analysis

基于两步聚类分析和UTR分析的低速率DoS攻击检测

Abstract The continuous advancement in the Internet of Things technology allows people to connect anywhere at any time, thus showing great potential in technology like smart devices (including smartphones and wearable devices). However, there is a possible risk of unauthorized access to these devices and technologies. Unfortunately, frequently used authentication schemes for protecting smart devices (such as passwords, PINs, and pattern locks) are vulnerable to many attacks. USB tokens and hardware keys have a risk of being lost. Biometric verification schemes are insecure as well as they are susceptible to spoofing attacks. Maturity in sensor chips and machine learning algorithms provides a better solution for authentication problems based on behavioral biometrics, which aims to identify the behavioral traits that a user possesses, such as hand movements and waving patterns. Therefore, this research study aims to provide a solution for passive and continuous authentication of smartphone users by analyzing their activity patterns when interacting with their phones. The motivation is to learn the physical interactions of a smartphone owner for distinguishing him/her from other users to avoid any unauthorized access to the device. Extensive experiments were conducted to test the performance of the proposed scheme using random forests, support vector machine, and Bayes net. The best average recognition accuracy of 74.97% is achieved with the random forests classifier, which shows the significance of recognizing smartphone users based on their interaction with the phones.

Mohammed A. Alqarni

Sajjad Hussain Chauhdary

Maryam Naseer Malik

Muhammad Ehatisham-ul-Haq

Muhammad Awais Azam

Identifying smartphone users based on how they interact with their phones

基于用户与手机互动方式识别智能手机用户

Abstract The quality and quantity of participation supplied by human beings during the different phases of the design and development of a software artifact are central to studies in human-centered computing. With this paper, we have investigated on what kind of experienced people should be engaged to design a new computational artifact, when a participatory approach is adopted. We compared two approaches: the former including only future users (i.e., novices ) in the design process, and the latter enlarging the community to expert users. We experimented with the design of a large software artifact, in use at the University of Bologna, engaging almost 1500 users. Statistical methodologies were employed to validate our findings. Our analysis has provided mounting evidence that expert users have contributed to the design of the artifact only by a small amount. Instead, most of the innovative initiatives have come from future users, thus surpassing some traditional limitations that tend to exclude future users from this kind of processes. We here challenge the traditional opinion that expert users provide typically a more reliable contribution in a participatory software design process, demonstrating instead that future users would be often better suited. Along this line of sense, this is the first paper, in the field of human-centric computing, that discusses the relevant question to offer to future users a larger design space, intended as a higher level of freedom given in a software design situation, demarcated by precise design constraints. In this sense, the outcome has been positive.

Marco Roccetti

Catia Prandi

Silvia Mirri

Paola Salomoni

Designing human-centric software artifacts with future users: a case study

以未来用户为中心设计软件制品：一个案例研究

Abstract Data fusion has always been a hot research topic in human-centric computing and extended with the development of artificial intelligence. Generally, the coupled data fusion algorithm usually utilizes the information from one data set to improve the estimation accuracy and explain related latent variables of other coupled datasets. This paper proposes several kinds of coupled images decomposition algorithms based on the coupled matrix and tensor factorization-optimization (CMTF-OPT) algorithm and the flexible coupling algorithm, which are termed the coupled images factorization-optimization (CIF-OPT) algorithm and the modified flexible coupling algorithm respectively. The theory and experiments show that the effect of the CIF-OPT algorithm is robust under the influence of different noises. Particularly, the CIF-OPT algorithm can accurately restore an image with missing some data elements. Moreover, the flexible coupling model has better estimation performance than a hard coupling. For high-dimensional images, this paper adopts the compressed data decomposition algorithm that not only works better than uncoupled ALS algorithm as the image noise level increases, but saves time and cost compared to the uncompressed algorithm.

Liangfu Lu

Xiaoxu Ren

Kuo-Hui Yeh

Zhiyuan Tan

Jocelyn Chanussot

Exploring coupled images fusion based on joint tensor decomposition

基于联合张量分解的耦合图像融合研究

F. Hao

D. S. Park

CoNavigator: A Framework of FCA-Based Novel Coronavirus COVID-19 Domain Knowledge Navigation

CoNavigator：基于FCA的新冠病毒COVID-19领域知识导航框架

Abstract The Internet of Things (IoT) is a paradigm that permits smart entities to be interconnected anywhere and anyhow. IoT opens new opportunities but also rises new issues. In this dynamic environment, trust is useful to mitigate these issues. In fact, it is important that the smart entities could know and trust the other smart entities in order to collaborate with them. So far, there is a lack of research when considering trust through the whole System Development Life Cycle (SDLC) of a smart IoT entity. In this paper, we suggest a new approach that considers trust not only at the end of the SDLC but also at the start of it. More precisely, we explore the modeling phase proposing a model-driven approach extending UML and SysML considering trust and its related domains, such as security and privacy. We propose stereotypes for each diagram in order to give developers a way to represent trust elements in an effective way. Moreover, we propose two new diagrams that are very important for the IoT: a traceability diagram and a context diagram. This model-driven approach will help developers to model the smart IoT entities according to the requirements elicited in the previous phases of the SDLC. These models will be a fundamental input for the following and final phases of the SDLC.

Davide Ferraris

Carmen Fernandez-Gago

Javier Lopez

A model-driven approach to ensure trust in the IoT

一种确保物联网信任的模型驱动方法

Abstract Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test–retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent.

Karolis Ryselis

Tautvydas Petkus

Tomas Blažauskas

Rytis Maskeliūnas

Robertas Damaševičius

Multiple Kinect based system to monitor and analyze key performance indicators of physical training

基于多Kinect系统的体能训练关键绩效指标监测与分析

Abstract IoT describes a new world of billions of objects that intelligently communicate and interact with each other. One of the important areas in this field is a new paradigm-Social Internet of Things (SIoT), a new concept of combining social networks with IoT. SIoT is an imitation of social networks between humans and objects. Objects like humans are considered intelligent and social. They create their social network to achieve their common goals, such as improving functionality, performance, and efficiency and satisfying their required services. Our article’s primary purpose is to present a comprehensive review article from the SIoT system to analyze and evaluate the recent works done in this area. Therefore, our study concentrated on the main components of the SIoT (Architecture, Relation Management, Trust Management, web services, and information), features, parameters, and challenges. To gather enough information for better analysis, we have reviewed the articles published between 2011 and December 2019. The strengths and weaknesses of each article are examined, and effective evaluation parameters, approaches, and the most used simulation tools in this field are discussed. For this purpose, we provide a scientific taxonomy for the final SIoT structure based on the academic contributions we have studied. Ultimately we observed that the evaluation parameters are different in each element of the SIoT ecosystem, for example for Relation Management, scalability 29% and navigability 22% are the most concentrated metrics, in Trust Management, accuracy 25%, and resiliency 25% is more important, in the web service process, time 23% and scalability 16% are the most mentioned and finally in information processing, throughput and time 25% are the most investigated factor. Also, Java-based tools like Eclipse has the most percentage in simulation tools in reviewed literature with 28%, and SWIM has 13% of usage for simulation.

Mozhgan Malekshahi Rad

Amir Masoud Rahmani

Amir Sahafi

Nooruldeen Nasih Qader

Social Internet of Things: vision, challenges, and trends

社交物联网：愿景、挑战与趋势

Abstract The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.

Abbreviation	Hum. Cent. Comput. Inf. Sci.
Journal Impact	3.74
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q2)

ISSN	2192-1962
h-index	52

Publication Information	Publisher: Korea Information Processing Society，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2011，Proportion of original research papers: 100.00%，Self Citation Rate:15.40%， Gold OA Rate: 0.00%
Average review cycle	网友分享经验：13 Weeks

Human-centric Computing and Information Sciences

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