Progress in the brain–computer interface: an interview with Bin He

What can the brain–computer interface (BCI) do? Wearing an electroencephalogram (EEG) headcap, you can control the flight of a drone in the laboratory by your thought; with electrodes inserted inside the brain, paralytic patients can drink by controlling a robotic arm with thinking. Both invasive and non-invasive BCI try to connect human brains to machines. In the past several decades, BCI technology has continued to develop, making science fiction into reality and laboratory inventions into indispensable gadgets. In July 2019, Neuralink, a company founded by Elon Musk, proposed a sewing machine-like device that can dig holes in the skull and implant 3072 electrodes onto the cortex, promising more accurate reading of what you are thinking, although many serious scientists consider the claim misleading to the public. Recently, National Science Review (NSR) interviewed Professor Bin He, the department head of Biomedical Engineering at Carnegie Mellon University, and a leading scientist in the non-invasive-BCI field. His team developed new methods for non-invasive BCI to control drones by thoughts. In 2019, Bin’s team demonstrated the control of a robotic arm to follow a continuously randomly moving target on the screen. In this interview, Bin He recounted the history of BCI, as well as the opportunities and challenges of non-invasive BCI.     

TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI

Brain–computer interface (BCI) is a promising intelligent healthcare technology to improve human living quality across the lifespan, which enables assistance of movement and communication, rehabilitation of exercise and nerves, monitoring sleep quality, fatigue and emotion. Most BCI systems are based on motor imagery electroencephalogram (MI-EEG) due to its advantages of sensory organs affection, operation at free will and etc. However, MI-EEG classification, a core problem in BCI systems, suffers from two critical challenges: the EEG signal’s temporal non-stationarity and the nonuniform information distribution over different electrode channels. To address these two challenges, this paper proposes TCACNet, a temporal and channel attention convolutional network for MI-EEG classification. TCACNet leverages a novel attention mechanism module and a well-designed network architecture to process the EEG signals. The former enables the TCACNet to pay more attention to signals of task-related time slices and electrode channels, supporting the latter to make accurate classification decisions. We compare the proposed TCACNet with other state-of-the-art deep learning baselines on two open source EEG datasets. Experimental results show that TCACNet achieves 11.4% and 7.9% classification accuracy improvement on two datasets respectively. Additionally, TCACNet achieves the same accuracy as other baselines with about 50% less training data. In terms of classification accuracy and data efficiency, the superiority of the TCACNet over advanced baselines demonstrates its practical value for BCI systems.     

SmartRolling: A human–machine interface for wheelchair control using EEG and smart sensing techniques

Smart wheelchairs based on brain–computer interface (BCI) have been widely utilized recently to address certain mobility problems for people with disability. In this paper, we present SmartRolling, an intuitive human–machine interaction approach for the direct control of robotic wheelchair that jointly leverages EEG signals and motion sensing techniques. Specifically, SmartRolling offers two wheelchair-actuation modes for users with different physical conditions: (1) head motion only — people who are severely disabled but able to do basic tasks using eyes and head, and (2) head and hands motion — in addition to type 1, people who can use functioning hands/arms for extra tasks. The system issues operation commands by recognizing different EEG patterns elicited by motor execution (ME) tasks including eye blink, jaw clench, and fist open/close, while at the same time estimates users’ steering intentions based on their facing direction by leveraging inertial measurements and computer vision techniques. The experiment results demonstrate that the proposed system is robust and effective to meets the individual’s needs and has great potential to promote better health.     

防范黑客入侵攻击电脑的主要方法

病毒破坏和黑客攻击是目前网络中危害电脑系统的两大不安全因素。黑客是破坏网络安全的根源。建立完善的保护系统,有效地防范黑客的恶意攻击,是维护电脑安全的根本所在。     

运动想象机制研究

运动想象已被广泛地应用在BCI系统上。传统对脑电信号分析主要集中在特征提取和分类上,本文分别从左右想象脑电信号的频域、时域和脑地形图上进行分析,从而获取左右想象脑电信号的特征。     

Improving information security management: An analysis of ID–password usage and a new login vulnerability measure

Statistics show that the number of identity theft victims in the US increased by 12% in 2009, to 11.1 million adults, while the total annual fraud amount increased by 12.5%, to $54 billion. As the e-commerce volume is increasing and various online services are becoming more popular, the number of sites to which an average Internet user subscribes is increasing rapidly. Given the limited memory capacity of human beings, an Internet user's login credentials (in the form of a combination of a user ID and a password) are usually reused over multiple accounts, which can cause significant security problems. In this study, we address the vulnerability of login credentials. First, based on a unique Internet user data set, we analyze the behavioral characteristics of login credentials usage. We find that the same login credentials are used for many more accounts and reused much more often than previously expected. Furthermore, usage patterns are found to be quite skewed. Second, building on a network perspective of login credentials usage, we suggest a vulnerability measure of an individual's login credentials and analyze the vulnerability of current Internet users. The resulting information is valuable not only to the research community but also to managers and policy makers striving to reduce security vulnerability.     

公用计算机网络安全威胁及防范措施

在网络日益普及的今天,公用计算机在保证日常工作进行的前提下,其安全问题也日益受到计算机用户的关注。本文分析影响计算机网络安全的主要因素。从计算机管理和技术两方面针对性地提出加强计算机网络安全的一些方法和防范措施。     

高校图书馆资源发现系统用户功能需求与差异性量化研究

资源发现系统是高校图书馆重要建设对象,用户对其需求显著且存在隐性差异,相关分析能为资源发现系统建设、利用与发展提供决策参考。研究者利用Citespace进行现状调研,引入马斯洛理论分析功能的资源安全获取、关联知识与分享、客观性知识分析等用户需求,选择典型用户为受测者,采用任务操作与李克特式问卷量表进行用户体验获得评分数据,同时利用脑电、皮电测量情绪提升体验质量,通过SPSS分析量化数据以得出结论。研究发现高校用户需求高低与各群体差异,据此给出若干建议。     

网络空间安全视阈下恶意软件攻防策略研究

网络空间安全是国家安全和经济安全的基础。本文基于恶意软件的两种攻击方式和三种用户防治策略,构建网络空间安全视阈下恶意软件攻防的系统动力学模型,对攻防组合策略进行了交叉分析,并用调研数据验证了模型的有效性。研究发现：在恶意软件的预防、应对阶段,安全教育和安全工具投资可抑制恶意软件传播,减少系统安全脆弱性;恢复阶段的成本投入可减少用户损失;针对不同的防治目标,用户应采取不同的组合防治策略。最后提出的对策建议为用户实施有效的恶意软件防治提供理论与实践指导。     

网络空间安全视阈下恶意软件攻防策略研究

网络空间安全是国家安全和经济安全的基础。本文基于恶意软件的两种攻击方式和三种用户防治策略，构建网络空间安全视阈下恶意软件攻防的系统动力学模型，对攻防组合策略进行了交叉分析，并用调研数据验证了模型的有效性。研究发现：在恶意软件的预防、应对阶段，安全教育和安全工具投资可抑制恶意软件传播，减少系统安全脆弱性；恢复阶段的成本投入可减少用户损失；针对不同的防治目标，用户应采取不同的组合防治策略。最后提出的对策建议为用户实施有效的恶意软件防治提供理论与实践指导。     

面向Linux的入侵检测系统数据包采集模块的研究与实现

近年来,随着网络技术的迅猛发展和因特网的广泛普及,信息与网络的安全形势日趋严峻和复杂化,各种计算机安全事件也不断发生。网络安全问题变得日益突出,NIDS用来监视网络数据流动情况,当入侵发生时能够提供报警。详细的分析了一个基于Linux的用于用户层包捕获的系统独立的API接口Libpcap库,并给出了利用Libpcap设计基于包捕获的应用程序的应用框架。最后对该框架进行了具体实现。     

计算机病毒入侵的途径与防治研究

计算机病毒往往会利用计算机操作系统的弱点进行传播,提高系统的安全性是防病毒的一个重要方面。但完美的系统是不存在的,过于强调提高系统的安全性将使系统多数时间用于病毒检查,系统失去了可用性、实用性和易用性。另一方面,信息保密的要求让人们在泄密和抓住病毒之间无法选择。     

Cracking down on autonomy: three challenges to design in IT Law

The paper examines how technology challenges conventional borders of national legal systems, as shown by cases that scholars address as a part of their everyday work in the fields of information technology (IT)-Law, i.e., computer crimes, data protection, digital copyright, and so forth. Information on the internet has in fact a ubiquitous nature that transcends political borders and questions the notion of the law as made of commands enforced through physical sanctions. Whereas many of today??s impasses on jurisdiction, international conflicts of law and diverging interpretations of statutes can be addressed by embedding legal safeguards in ICT and other kinds of technology, to overcome the ineffectiveness of state action by design entails its own risks, e.g., threats of paternalism hinging on the regulatory tools of technology. Rather than modelling people??s behaviour by design, the article suggests that design policies should respect individual and collective autonomy by decreasing the impact of harm-generating behaviour (e.g., security measures and default settings for data protection), or by widening the range of people??s choices (e.g., user friendly interfaces).     

网络监听技术及其防范措施研究

随着科学技术的不断发展,计算机网络已经深入到人们生活的各个方面,它在给人们的生活、工作带来便捷的同时,也衍生出很多问题,如网络安全、用户信息的隐私性等。其中对用户隐私危害最大的就是网络监听,用户通过网络发送或接受的信息都会传送给监听者,对用户的网络安全造成极大威胁。介绍了网络监听技术及基本原理,重点研究了网络监听技术及其防范措施,以保障网络的安全运行。     

Design and evaluation of awareness mechanisms in CiteSeer

Awareness has been extensively studied in human computer interaction (HCI) and computer supported cooperative work (CSCW). The success of many collaborative systems hinges on effectively supporting awareness of different collaborators, their actions, and the process of creating shared work products. As digital libraries are increasingly becoming more than just repositories for information search and retrieval – essentially fostering collaboration among its community of users – awareness remains an unexplored research area in this domain. We are investigating awareness mechanisms in CiteSeer, a scholarly digital library for the computer and information science domain. CiteSeer users can be notified of new publication events (e.g., publication of a paper that cites one of their papers) using feeds as notification systems. We present three cumulative user studies – requirements elicitation, prototype evaluation, and naturalistic study – in the context of supporting CiteSeer feeds. Our results indicate that users prefer feeds that place target items in query-relevant contexts, and that preferred context varies with type of publication event. We found that users integrated feeds as part of their broader, everyday activities and used them as planning tools to collaborate with others.     

The effects of communication monitors on user satisfaction

This study analyzes the influence of communication monitors (CM) on overall computer user satisfaction as determined by multiple regression. The variables user expectations, manufacturers and vendors are found to be the most significant variables affecting overall satisfaction, whereas the variables number of systems represented and not using communication monitors have the least significant effect in the user satisfaction model. The implications for improvements in CM by updates to a database are discussed and analyzed.     

“互联网+”物流信息安全管理的用户行为路径优化研究

对"互联网+"物流信息安全管理的用户行为路径进行优化研究,希望通过显性化的特征值来揭示物流信息安全管理的风险程度。通过构建供应链模型,采用TSP算法求解供应链上的物流业务最短路径,认为无论节点的用户行为如何表现,其物流业务运作必然依托原有的供应链最短路径结构开展。以物流信息安全管理背后的用户行为为载体对象,构建变异的用户行为熵TSP模型。将网络的信息安全问题抽象为求不重复连接所有节点用户熵值的最短路径,在此基础上,对用户行为路径进行仿真优化,并得出相关的结论。     

Android手机安全防护系统

Android成为活跃用户量最大的移动终端操作系统的同时,其安全问题日益明显。特别是联系人、短信、照片与我们的个人隐私紧密相连,一旦手机被盗,这些隐私都被违法分子掌握利用,所造成的损失将是无法估计的。文章研究文件监控、手机防盗、短信加密等三方面的安全防护以保障用户的个人隐私和财产安全,为Android手机用户提供了有效的安全保障,并具有极大的应用潜力。     

Library-user interface: A simulation of the circulation subsystem

The loan policy of a university library can have a significant effect on the ability of library users to find the particular books that they are seeking. This paper focuses on the library-user interface by relating two measures of user satisfaction, book availability and the delay associated with recalls, to the loan policy of the library. This relation is established by computer simulation techniques. This paper synthesizes the results of two studies conducted at the Sears Library at Case Western Reserve University. The results of these studies confirm that the model can accurately predict two measures of user satisfaction as a function of the load policy of a library. Thus this technique can be used to evaluate alternative loan policies in terms of their impact on user satisfaction.