跨越软件工程教学到实践的鸿沟

介绍目前软件工程教学普遍存在的与实践相脱节现象,分析软件工程教学的现实困难,提出要以"学以致用"为原则,优化课程体系设计,合理衔接基础课程、综合性课程、技能课程。阐述以社会需求为导向,设置实践教学内容,加强师资教材建设,校企合作,提炼经典规范案例;采用团队分组与固定结对搭档相结合的组织方式,实行组内、组间两级全员评审的考核模式,促进学生在思考中学习提高。     

Why Teachers Must Learn: Student Innovation as a Driving Factor in the Future of the Web

User innovation is the key to the development and vitality of technology. As Huatong Sun wrote, “expanding the scope of localization practices and linking user localization efforts” to design cycles—including pedagogical design—will help bridge the gap between what teachers/designers create and what skills users/students need and want. This article theorizes the role of modern composition students and teachers as co-constructors of productive spaces for learning critical inquiry based on students’ statuses as digital natives. It focuses on a class of first-year composition students who reshaped an assignment to fit their own needs within the physical classroom and also enacted a shift to a virtual classroom. While doing so, they provided ways for each class member to individualize space within that digital environment, and they focused their project on examining the ways in which social networking forums colonize their daily lives. This article argues that by letting student innovation drive pedagogical practice—just as social media creators let user innovation drive the digital structures they produce—composition teachers can be assured of having a text for critique that blurs the lines between “private” student underlife and “public” classroom practice and legitimizes the creation of student-produced learning spaces. Using critical and cyberfeminist theories as lenses, this article draws conclusions about the future of computer usage in composition pedagogy based on students’ abilities to re-appropriate physical and digital classroom space.     

Herbert A. Simon: AI pioneer

Herbert A. Simon was one of the founding fathers of artificial intelligence. Simon, who, along with Allen Newell and J.C. Shaw, wrote the first AI program in 1956, received many honors in his lifetime, including the Nobel Prize in Economic Science in 1978. Simon was a true renaissance man. He researched human decision-making and problem-solving processes and the implications of those processes for social institutions. Simon's major awards in four areas: psychology, economics, management science and computer science - attest to his breadth of interest and expertise. Simon's impact in the world is no more apparent than at Carnegie Mellon University, where, arguably, he was the most influential person. Simon left behind over 60 years of research and theorems. He also left the computer science world with the heuristic compiler, in which he applied theories and techniques from other disciplines.     

Personal information retrieval: smartphones vs. computers,emails vs. files

Millions of people retrieve their emails and files using their smartphones, yet smartphone retrieval of such personal information has never been studied or compared to retrievals from PCs. In our within-subjects study, we compared the retrievals of our 57 participants in four conditions: files using PCs, emails using PCs, files using smartphones, and emails using smartphones. Our results indicate that when using smartphones, retrievals were significantly less successful and efficient than when using PCs, casting doubt on the implicit assumption that the use of these devices is equivalent. Our results also indicate that participants used the search facility for emails about seven times more than for files, which can encourage vendors to invest more efforts in improving email search engines and file navigation systems. Finally, we found that the tendency to search shows interpersonal differences but consistency across different situations for the same individual and therefore can be regarded as a personal trait. Future research can attempt to explain the search tendency trait in terms of cognitive abilities and personality traits, incorporating it to well-established theories. This may pave the way to a new trait-related theory in the field of information science.     

CAPTCHAs: Humans vs. Bots

A completely automated public turing test to tell computers and humans apart (CAPT-CHA) offers a way for Web service providers to make some conclusions about whether a "user" is human or robot. Process of CAPTCHA recognition is a combination of efforts, approaches, and software that attempts to increase accuracy to an acceptable level. Of course, it's hard to define this level, but we've found that it often starts from 5 percent accuracy. We've covered the most popular CAPTCHA approaches here but not all of them. Most users know how to use the more commonly used graphical CAPTCHAs, so designers must have serious reasons to migrate to other, more difficult-to-break, approaches. After all, CAPTCHAs aim to determine legitimate users, not alienate them.     

Developing the requirements discipline: software vs. systems

The software engineering and systems engineering communities are establishing methods for capturing, specifying, and managing requirements. The cultural differences exist between the two. Requirements work is too important to be pioneered by a mainstream software-centric or -ignorant viewpoint. Each community must continue to learn from the other for our understanding of the requirements engineering discipline to continue to grow.     

UML vs. classical vs. rhapsody statecharts: not all models are created equal

State machines, represented by statecharts or state machine diagrams, are an important formalism for behavioural modelling. According to the research literature, the most popular statechart formalisms appear to be Classical, UML, and that implemented by Rhapsody. These three formalisms seem to be very similar; however, there are several key syntactic and semantic differences. These differences are enough that a model written in one formalism could be ill-formed in another formalism. Worse, a model from one formalism might actually be well-formed in another, but be interpreted differently due to the semantic differences. This paper summarizes the results of an informal comparative study of these three formalisms with the help of several illustrative examples. We present a classification of the differences according to the nature of potential problems caused by each difference. In addition, for each difference we discuss how translation between formalisms can be achieved, if at all.     

AI and Law: What about the future?

The introduction of results of AI and Law research in actual legal practice advances disturbingly slow. One of the problems is that most research can be classified as either theoretical or pragmatic, while combinations of these two are scarce. This interferes with the need for feedback as well as with the need of getting support, both financially and from actual legal practice. The conclusion of this paper is that an emphasis on research that generates operational and sophisticated systems is necessary in order to provide a future for AI and Law.     

Special Feature: Nanoprogramming vs. Microprogramming

Vertical microprogramming (or, as others say, functional microprogramming or firmware) was introduced in contrast with Wilkes' microprogramming (generally called horizontal or structural microprogramming), and has greatly enhanced the use of microprogramming in control design and implementation. As applied today, the former technique is not yet a way of designing control structures, but an additional software level whose implications in the system are not well clarified.     

Plant identification: man vs. machine

This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of diverse expertise, ranging from the leading experts of the targeted flora to inexperienced test subjects. In total, 16 human runs were collected and evaluated comparatively to the 27 machine-based runs of LifeCLEF challenge. One of the main outcomes of the experiment is that machines are still far from outperforming the best expert botanists at the image-based plant identification competition. On the other side, the best machine runs are competing with experienced botanists and clearly outperform beginners and inexperienced test subjects. This shows that the performances of automated plant identification systems are very promising and may open the door to a new generation of ecological surveillance systems.     

RFID malware: truth vs. myth

On 15 March 2006, our research team at Vrije Universiteit published a paper about RFID malware entitled "Is Your Cat Infected with a Computer Virus?" as well as a companion Web site (www.rfidvirus.org). Our paper introduced the concept of RFID malware and presented an accompanying proof-of-concept RFID virus. The paper ultimately resulted in a huge amount of media attention; within 24 hours of presenting it at the Fourth Annual IEEE International Conference on Pervasive Computing and Communications (IEEE PerCom), we received more than 200 email messages. Amid this chaos, our research paper received the conference's best paper award for high impact. In the months that followed, reports of RFID malware prompted reactions from the RFID industry, the antivirus industry, and the US and Dutch governments.     

DC测量：电压与电流

这解释了为什么电压测量成为长距离应用中较常用的一种方式。例如,如果你想从一个单独的控制室测量通过直流电机的电流,你需要一种将电流测量转换为电压测量的方法。     

The academic quality of AI journals and the role of AI in the MIS curriculum: Perspectives of business faculty

In this paper, we report the results of the first phase of a three-part study of the emerging role of Artificial Intelligence (AI) as an academic discipline. The stratification among journals that are formal communication vehicles for AI research is described and analyzed. Based on a survey of business faculty from AACSB-accredited schools around the country, 30 leading AI journals are ranked and evaluated, in terms of their academic quality and reputation. The paper also reports on the progress toward the development of a cumulative tradition for AI and its foundation. The second phase of this research focuses on the knowledge utilization of AI researchers and practitioners. In the third phase, the findings from the above two studies will be analyzed and used as a basis to develop a taxonomy of research for Artificial Intelligence.