AI, E-government, and Politics 2.0

This issue's Trends and Controversies department includes five essays on e-government and politics 2.0 from distinguished experts. Each essay presents a unique, innovative research framework, computational methods, and selected results and examples. As the government and political process become more transparent, participatory, online, and multimedia rich, there is a great opportunity for adopting advanced AI and intelligent systems research in e-government and politics 2.0 applications. Selected techniques in data, text, Web, and opinion mining, social network analysis, visual analytics, multimedia analysis, ontological representations, and social media analysis can support online political participation, e-democracy, political blogs and forums, e-government service delivery, and transparency and accountability.     

Evaluation of handle shapes for screwdriving

This study investigated the effects of screwdriver handle shape, surface, and workpiece orientation on subjective discomfort, number of screw-tightening rotations, screw-insertion time, axial screwdriving force, and finger contact forces in a screwdriving task. Handles with three longitudinal cross-sectional shapes (circular, hexagonal, triangular), four lateral shapes (cylindrical, double frustum, reversed double frustum, cone), and two surface materials (plastic, rubber coated) were tested. Individual phalangeal segment force distributions indicated how fingers and phalangeal segments were involved in the creation of total finger force (15.0%, 34.6%, 34.5%, and 15.9% for the index, middle, ring, and little fingers; and 45.7%, 22.4%, 12.9%, and 19.0% for the distal, middle, proximal, and metacarpal phalanges, respectively). From this finding, the index and little fingers appeared to contribute mainly in the guiding and balancing of the screwdriver handles, whereas middle and ring fingers played a more prominent role in gripping and turning. Participants preferred circular and hexagonal longitudinal-shaped and double frustum and cone lateral-shaped handles over the triangular longitudinal-shaped handles, and cylindrical and reversed double frustum lateral-shaped handles. Circular, cylindrical, and double frustum handles exhibited the least total finger force associated with screw insertion. In terms of combinations of longitudinal and lateral shapes, circular with double frustum handles were associated with less discomfort and total finger force.     

Laser and Photonic Systems Integration: Emerging Innovations and Framework for Research and Education

The purpose of this article is to review the key emerging innovations in laser and photonics systems as well as their design and integration, focusing on challenges and opportunities for solutions of societal challenges. Developments, their significance, and frontier challenges are explained in advanced manufacturing, biomedicine and healthcare, and communication. Systems, networks, and integration issues and challenges are then discussed, and an integration framework for networking laser‐ and photonic‐based services and products is proposed. The article concludes with implications and an agenda for education, research and development, and policy needs, with a focus on human, society, science, and technology integration. © 2013 Wiley Periodicals, Inc.     

基于DS18B20的数字式温度采集报警系统设计

介绍了一种基于AT89S52单片机和DS18B20温度传感器的温度采集和报警系统,对方案设计、元器件选型、硬件结构和软件设计等几部分内容进行了详细说明。硬件结构由电源模块、测温模块、报警模块、显示模块和按键模块五大模块组成,软件给出了DS18B20初始化时序、读写时序和温度采集程序。该系统能实现温度采集、显示、报警和报警限设置功能,广泛应用于仓储、农业和运输等。经过测试,该系统具有良好的稳定性和精度,抗干扰能力强,易于扩展且实用。     

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

     

Machine learning models and bankruptcy prediction

There has been intensive research from academics and practitioners regarding models for predicting bankruptcy and default events, for credit risk management. Seminal academic research has evaluated bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks). In this study, we test machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event, and compare their performance with results from discriminant analysis, logistic regression, and neural networks. We use data from 1985 to 2013 on North American firms, integrating information from the Salomon Center database and Compustat, analysing more than 10,000 firm-year observations. The key insight of the study is a substantial improvement in prediction accuracy using machine learning techniques especially when, in addition to the original Altman’s Z-score variables, we include six complementary financial indicators. Based on Carton and Hofer (2006), we use new variables, such as the operating margin, change in return-on-equity, change in price-to-book, and growth measures related to assets, sales, and number of employees, as predictive variables. Machine learning models show, on average, approximately 10% more accuracy in relation to traditional models. Comparing the best models, with all predictive variables, the machine learning technique related to random forest led to 87% accuracy, whereas logistic regression and linear discriminant analysis led to 69% and 50% accuracy, respectively, in the testing sample. We find that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. Our research adds to the discussion of the continuing debate about superiority of computational methods over statistical techniques such as in Tsai, Hsu, and Yen (2014) and Yeh, Chi, and Lin (2014). In particular, for machine learning mechanisms, we do not find SVM to lead to higher accuracy rates than other models. This result contradicts outcomes from Danenas and Garsva (2015) and Cleofas-Sanchez, Garcia, Marques, and Senchez (2016), but corroborates, for instance, Wang, Ma, and Yang (2014), Liang, Lu, Tsai, and Shih (2016), and Cano et al. (2017). Our study supports the applicability of the expert systems by practitioners as in Heo and Yang (2014), Kim, Kang, and Kim (2015) and Xiao, Xiao, and Wang (2016).     

Engineering complex computer systems: a challenge for computertypes everywhere. I. Let's agree on what these systems are

We define the engineering of complex computer systems as all activities pertinent to specifying, designing, prototyping, building, testing, operating, maintaining, and evolving complex computer systems. While in the past, relatively noncomplex traditional systems sufficed for most computer control applications, the new and emerging demands of applications and the evolution of computer architectures and networks now essentially force systems to be complex, given our current understanding of how to engineer these systems. Complex computer systems are found in almost every industry. These include industrial process control, aerospace and defence, transportation and communications, energy and utilities, medicine and health, commercial data processing, and others. Unfortunately, the state of the art in research and technology has clearly fallen far behind the requirements of industrial, commercial, and government complex computer systems     

Agent-based control for networked traffic management systems

Agent or multiagent systems have evolved and diversified rapidly since their inception around the mid 1980s as the key concept and method in distributed artificial intelligence. They have become an established, promising research and application field drawing on and bringing together results and concepts from many disciplines, including AI, computer science, sociology, economics, organization and management science, and philosophy. However, multiagent systems have yet to achieve widespread use for controlling traffic management systems. Most research focuses on developing hierarchical structures, analytical modeling, and optimized algorithms that are effective for real-time traffic applications, as you can see from well-known traffic control systems such as CRONOS, OPAC, SCOOT, SCAT, PRODYN, and RHODES. Although those functional-decomposition-based systems are useful and successful for many traffic management problems, costs and difficulties associated with their development, operation, maintenance, expansion, and upgrading are often prohibitive and sometimes unnecessary, especially in the rapidly arriving age of connectivity. We need to rethink control systems and reinvestigate the use of simple task-oriented agents for traffic control and management of transportation systems.     

A microcomputer system for collecting and processing physiological information about the masticatory system.

A software system has been written for IBM PC, XT, AT and compatible computers to be used for data collection, analysis and display. The system supports the sampling and processing of data for jaw movement tracks, myoelectrical activities of masticatory muscles, the temporomandibular joint (TMJ) and occlusal sounds and bite force, etc. The package includes the following functions: calculating displacement, velocity, curvature, and curvature center of jaw movement trace, analyzing myoelectrical signals in amplitude integration, root mean square and power spectrum, processing TMJ sounds and occlusal sounds and bite force, analysing jaw movement traces and myoelectrical activities during mastication simultaneously, determining the maxillomandibular relations, etc. The program also provides versatile formatting capability for video, printing and plotting of data, and graph creation. The most of the above programs have flexibility and adaptability to other physiological signal processes.     

From research to classroom a course in pervasive computing

In a nutshell, the course is basically about emerging, visible and invisible computing systems and devices. Pervasive computing has many names, including ubiquitous computing, and its key element is the omnipresence of information devices. These devices can be embedded into cars, airplanes, ships, bikes, posters, signboards, walls, and even clothes. The course therefore focuses on independent information devices, including wearable computers, mobile phones, screen phones, and PDAs, and the services made available by them. It includes the study of computer and network architectures for pervasive computing, mobile computing, human-computer interaction using speech and vision, pervasive software systems, and experimental pervasive computing systems.     

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

Activity detection and classification using different sensor modalities have emerged as revolutionary technology for real-time and autonomous monitoring in behaviour analysis, ambient assisted living, activity of daily living (ADL), elderly care, rehabilitations, entertainments and surveillance in smart home environments. Wearable devices, smart-phones and ambient environments devices are equipped with variety of sensors such as accelerometers, gyroscopes, magnetometer, heart rate, pressure and wearable camera for activity detection and monitoring. These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. Recently, deep learning algorithms for automatic feature representation have also been proposed to lessen the burden of reliance on handcrafted features and to increase performance accuracy. Initially, one set of sensor data, features or classifiers were used for activity recognition applications. However, there are new trends on the implementation of fusion strategies to combine sensors data, features and classifiers to provide diversity, offer higher generalization, and tackle challenging issues. For instances, combination of inertial sensors provide mechanism to differentiate activity of similar patterns and accurate posture identification while other multimodal sensor data are used for energy expenditure estimations, object localizations in smart homes and health status monitoring. Hence, the focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices. First, data fusion methods and modalities were presented and also feature fusion, including deep learning fusion for human activity recognition were critically analysed, and their applications, strengths and issues were identified. Furthermore, the review presents different multiple classifier system design and fusion methods that were recently proposed in literature. Finally, open research problems that require further research and improvements are identified and discussed.     

面向虚实融合的人机交互

面向虚实融合的人机交互涉及计算机科学、认知心理学、人机工程学、多媒体技术和虚拟现实等领域，旨在提高人机交互的效率，同时响应人类认知与情感的需求，在办公教育、机器人和虚拟/增强现实设备中都有广泛应用。本文从人机交互涉及感知计算、人与机器人交互及协同、个性化人机对话和数据可视化等4个维度系统阐述面向虚实融合人机交互的发展现状。对国内外研究现状进行对比，展望未来的发展趋势。本文认为兼具可迁移与个性化的感知计算、具备用户行为深度理解的人机协同、用户自适应的对话系统等是本领域的重要研究方向。     

一体化综合管控技术在茨淮新河灌区的建设研究

针对茨淮新河灌区信息化涉及多个专业、监控类型及对象多等问题,设计具有多元物联测控和低功耗的一体化综合管控硬件平台,研制智能装置与产品,实现灌区水情、闸泵站等多业务的综合监控,并建立集全面感知、精准配水、协同管控于一体的灌区一体化综合管控软件平台。在茨淮新河灌区通过一体化综合管控硬件和软件平台的应用,构建一个以灌区工程核心业务为需求的、通用的、规范的、适应未来发展的灌区信息化综合管控系统,覆盖水量监测、视频监视、调度控制、生产运行等全业务,解决传统灌区自动化系统技术差异大、资源共享困难、业务协同性差及运行管理效率低等问题,提高灌区水资源高效利用和管理水平,形成农业水资源可持续利用新型模式。     

区块链技术发展现状与展望

区块链是随着比特币等数字加密货币的日益普及而逐渐兴起的一种全新的去中心化基础架构与分布式计算范式, 目前已经引起政府部门、 金融机构、 科技企业和资本市场的高度重视与广泛关注. 区块链技术具有去中心化、 时序数据、 集体维护、 可编程和安全可信等特点, 特别适合构建可编程的货币系统、 金融系统乃至宏观社会系统. 本文通过解构区块链的核心要素, 提出了区块链系统的基础架构模型, 详细阐述了区块链及与之相关的比特币的基本原理、 技术、 方法与应用现状, 讨论了智能合约的理念、 应用和意义, 介绍了基于区块链的平行社会发展趋势, 致力于为未来相关研究提供有益的指导与借鉴.     

气动数据可视化系统的设计与实现

针对气动数据高维、量大、来源广泛的特点以及使用单机工具进行数据分析功能单一的问题,在已有飞行器气动数据的基础上,设计并实现了基于MVVM模式的气动数据可视化分析系统;该系统基于气动试验、仿真、处理与分析的领域类数据语义化,通过MVVM框架的动态模板建立了灵活、快速的气动数据分组及聚合机制,并同时适配气动力、热、压的可视化分析需求,动态生成通用的交互与可视化UI组件,通过对系统进行不同参数的选取及条件值设置来进行气动数据的分析、处理与结果的可视化,从而提高研究人员的工作效率;实际应用表明,该系统界面友好,性能稳定,能简单快捷地通过动态生成的数据筛选条件进行分析可视化的操作,满足研究人员的需求。     

单片机与LoRa通信模块的电气火灾探测器设计

本文设计了一套基于单片机与LoRa组合式电气火灾探测器。探测器设计了电压采集调理电路、电流采集调理电路、剩余电流采集调理电路及温度采集调理电路,检测线路中的三相电压、三相电流、剩余电流及温度等电参量与温度参量,若线路存在过欠电压、短路、开路、漏电及温度异常等电气故障,现场立即发出声光报警且显示其故障信息,并通过LoRa无线通信模块将实时信息上传到云服务平台。故障信息通过电脑与手机及时推送给用户,用户对其进行远程监管与控制。测试结果表明,基于单片机与LoRa的组合式电气火灾探测器反应灵敏、稳定性好,可在大型建筑商业住宅场所广泛使用。     

基于多源数据整合的河湖水质智能应用研究

为改善上海市河道水环境,针对现有河湖水质存在的问题,依靠信息化和数字化技术,汇聚各类水质相关数据,开展数据整合与分析,推出河道水质变化三级报警预警,建立相应业务处置机制和流程,探索河湖水质智能化应用,重点研究河湖水质数据监测、变化趋势分析、波动报警预警、整改处置和结果反馈等5个环节闭环管理.通过河湖水质智能应用研究,并在上海市河长制办公室工作平台和城市运行"一网统管"水务专题中进行实际应用.实际应用表明:河湖水质智能应用可有效发挥作用,强化对河湖水质恶化等问题的持续跟踪和有效监督,提高河湖水质治理效率,推进河湖协同治理,有效改善河湖水环境质量.     

DNS信道传输加密技术: 现状、趋势和挑战

DNS作为重要的互联网基础设施, 其明文传输的特点带来很多隐私安全风险. DoH、DoT、DoQ等DNS信道传输加密技术致力于防止DNS数据被泄露或篡改, 并保证DNS消息来源的可靠性. 首先从DNS消息格式、数据存储和管理、系统架构和部署等6个方面分析明文DNS存在的隐私安全问题, 并对已有的相关技术和协议进行总结. 其次分析DNS信道传输加密技术的实现原理及应用现状, 进而基于多角度评测指标对各加密协议在不同网络条件下的性能表现进行讨论. 同时通过填充机制的局限性、加密流量识别和基于指纹的加密活动分析等方向探讨DNS信道传输加密技术的隐私保护效果. 此外从部署规范、恶意流量对加密技术的利用和攻击、隐私和网络安全管理之间的矛盾, 以及加密后影响隐私安全的其他因素等方面总结DNS信道传输加密技术存在的问题、挑战和相关解决方案. 最后总结加密DNS服务的发现、递归解析器到权威服务器之间的加密、服务器端的隐私保护、基于HTTP/3的DNS等后续需要着重关注的研究方向.     

新闻推荐算法可信评价研究

随着AI、5G、AR/VR等新技术的快速发展,内容类应用如电子商务、社交网络、短视频等层出不穷,导致信息过载问题日益严重。人工智能技术的发展推动了智能算法的爆炸式运用,作为智能算法的一种,推荐算法在大数据、应用场景和计算力的推动下,通过信息过滤技术,为用户提供适应兴趣及行为的个性化及高质量的推荐服务,逐步提高了用户的使用体验、内容分发效率,在一定程度上缓解了信息过载的问题。但推荐算法的潜在偏见、黑盒化特性及内容分发方式也逐渐带来了决策结果不公平性、不可解释性,信息茧房、侵犯用户隐私等安全挑战。如何提高推荐算法的可解释性、公平性、可信程度等越来越受到国内外政府监管部门、产业及学术界的重点关注,推荐系统和推荐算法也由此从发展期进入管制期。为此,本文针对新闻推荐领域,分析推荐算法的稿件画像、用户画像、推荐推送、反馈干预和人工复审等关键要素,围绕推荐算法生态的参与者,如内容生产者、受众、算法模型、新闻平台,从公平性、可解释性和抗抵赖性三个方面提出了一种新闻推荐算法可信评价体系,并进行定量或定性分析。公平性、可解释性和抗抵赖性是正相关关系,当公平性和抗抵赖性越强、可解释程度越高,新闻推荐算法的可信度越高。希望弥补新闻推荐算法领域的可信研究的空白,建立可信推荐算法生态,加速安全推荐系统的建立和推广,同时为智能算法可信研究提供参考,为智能算法的监管和治理提供思路。