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No Sense of Place: The Impact of Electronic Media on Social Behavior.

Code and Other Laws of Cyberspace. Lawrence Lessig. New York: Basic Books, 1999. 230 pp. $21 hbk. Lawrence Lessig's Code and Other Laws of Cyberspace is perhaps the most original book yet written about cyberspace law. With an accessible style that is rich in anecdotes and metaphors and surprisingly low on legal and technical jargon, Lessig, a constitutional law scholar at Harvard and a consultant on the Microsoft antitrust case, writes for a wide scholarly audience. Even more than the recent The Control Revolution (New York: Century Foundation, 1999) by Andrew Shapiro, Lessig has managed to write about a rapidly evolving subject at a level of abstraction and theoretical sophistication that ensures that his contribution will long remain relevant. Central to Lessig's theoretical framework is his breakdown of four modalities of regulation: law, economic markets, norms, and an intriguing category he calls "architecture." Architecture includes constraints that the natural world imposes or that people construct-for example, speed bumps on roads. The architecture in cyberspace is computer code: code constructs the cyber-world. Consider the example of pornography regulation. Lessig contrasts how these four modalities of regulation control access to pornography in "real space" and in cyberspace, vividly demonstrating why a simple translation of existing law from "real space" to cyberspace is often impossible. In real space, pornography is extensively regulated through all four modalities, in ways that limit children's access to it. Laws require that vendors sell pornography only to adults. Markets prevent children from accessing pornography because it costs money. Norms help limit children's access to pornography, both by stigmatizing pornography and stigmatizing dealers who would sell it to children. In the category of real space "architecture," there is the simple matter of how a child is: even if a child attempted to disguise himself as an adult, he would probably notbe successful at fooling a salesperson. These four kinds of constraints may be imperfect, but are reasonably effective in limiting children's access to pornography in real space, Lessig contends. In cyberspace, however, the modalities of regulation of pornography operate differently and less effectively. Law is unsettled. Markets are different in at least two ways: distribution of digital files is much cheaper than distribution of printed material or videos, and though there are plenty of commercial pornography sites, much pornography can be accessed at no cost through newsgroups. Differences in the "architecture" of cyberspace are crucial because they allow faceless transactions. Faceless transactions may make disguising identity and age simple, therefore allowing a child to avoid the restrictions of law and norms. Thus the "real space" regulatory regime is upset, making pornography generally more available to children in cyberspace. Ways of filtering content or zoning the Internet are now extensively debated, but no simple transfer of the offline constraints to cyberspace seems possible. As he applies his theory to pornography regulation and the regulation of intellectual property and privacy, Lessig emphasizes that not only the codes of law but computer codethe "architecture" of cyberspace-an "embed" values and enable government and private actors to control behavior. …

Code and Other Laws of Cyberspace

Every design problem begins with an effort to achieve fitness between two entities: the form in question and its context. The form is the solution to the problem; the context defines the problem. We want to put the context and the form into effortless contact or frictionless coexistence, i.e., we want to find a good fit. For a good fit to occur in practice, one vital condition must be satisfied. It must have time to happen. In slow-changing, traditional, unselfconscious cultures, a form is adjusted soon after each slight misfit occurs. If there was good fit at some stage in the past, no matter how removed, it will have persisted, because there is an active stability at work. Tradition and taboo dampen and control the rate of change in an unselfconscious culture's designs. It is important to understand that the individual person in an unselfconscious culture needs no creative strength. He does not need to be able to improve the form, only to make some sort of change when he notices a failure. The changes may not always be for the better; but it is not necessary that they should be, since the operation of the process allows only the improvements to persist. Unselfconscious design is a process of slow adaptation and error reduction. In the unselfconscious process there is no possibility of misconstruing the situation. Nobody makes a picture of the context, so the picture cannot be wrong. But the modern, selfconscious designer works entirely from a picture in his mind - a conceptualization of the forces at work and their interrelationships - and this picture is almost always wrong. To achieve in a few hours at the drawing board what once took centuries of adaptation and development, to invent a form suddenly which clearly fits its context - the extent of invention necessary is beyond the individual designer. A designer who sets out to achieve an adaptive good fit in a single leap is not unlike the child who shakes his glass-topped puzzle fretfully, expecting at one shake to arrange the bits inside correctly. The designer's attempt is hardly as random as the child's is; but the difficulties are the same. His chances of success are small because the number of factors which must fall simultaneously into place is so enormous. The process of design, even when it has become selfconscious, remains a process of error-reduction. No complex system will succeed in adapting in a reasonable amount of time or effort unless the adaptation can proceed component by component, each component relatively independent of the others. The search for the right components, and the right way to build the form up from these components, is the greatest challenge faced by the modern, selfconscious designer. The culmination of the modern designer's task is to make every unit of design both a component and a system. As a component it will fit into the hierarchy of larger components that are above it; as a system it will specify the hierarchy of smaller components of which it itself is made.

Notes on the Synthesis of Form. By Christopher Alexander. Pp. 191. 54s. 1964. (Harvard University Press.)

The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigated research in the application of advanced machine learning algorithms such as deep learning to help with diagnostic and prescriptive analysis of causes and preventive measures. However, the publicity created by tech firms like Google, Facebook and Amazon about Artificial Intelligence and applications to unstructured data is not the end of the field. There abound many applications of deep learning, particularly within the construction sector in areas such as site planning and management, health and safety and construction cost prediction, which are yet to be explored. The overall aim of this article was to review existing studies that have applied deep learning to prevalent construction challenges like structural health monitoring, construction site safety, building occupancy modelling and energy demand prediction. To the best of our knowledge, there is currently no extensive survey of the applications of deep learning techniques within the construction industry. This review would inspire future research into how best to apply image processing, computer vision, natural language processing techniques of deep learning to numerous challenges in the industry. Limitations of deep learning such as the black box challenge, ethics and GDPR, cybersecurity and cost, that can be expected by construction researchers and practitioners when adopting some of these techniques were also discussed.

Deep Learning in the Construction Industry: A Review of Present Status and Future Innovations

Cloud computing in construction industry: Use cases, benefits and challenges

In the early diagnosis of the Coronavirus disease (COVID-19), it is of great importance for either distinguishing severe cases from mild cases or predicting the conversion time that mild cases would possibly convert to severe cases. This study investigates both of them in a unified framework by exploring the problems such as slight appearance difference between mild cases and severe cases, the interpretability, the High Dimension and Low Sample Size (HDLSS) data, and the class imbalance. To this end, the proposed framework includes three steps: (1) feature extraction which first conducts the hierarchical segmentation on the chest Computed Tomography (CT) image data and then extracts multi-modality handcrafted features for each segment, aiming at capturing the slight appearance difference from different perspectives; (2) data augmentation which employs the over-sampling technique to augment the number of samples corresponding to the minority classes, aiming at investigating the class imbalance problem; and (3) joint construction of classification and regression by proposing a novel Multi-task Multi-modality Support Vector Machine (MM-SVM) method to solve the issue of the HDLSS data and achieve the interpretability. Experimental analysis on two synthetic and one real COVID-19 data set demonstrated that our proposed framework outperformed six state-of-the-art methods in terms of binary classification and regression performance.

Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data

Redundant features and outliers (noise) included in the data points for a machine learning clustering model heavily influences the discovery of more distinguished features for clustering. To solve this issue, we propose a spectral new clustering method to consider the feature selection with the $$L_{2,1}$$
 -norm regularization as well as simultaneously learns orthogonal representations for each sample to preserve the local structures of data points. Our model also solves the issue of out-of-sample, where the training process does not output an explicit model to predict unseen data points, along with providing an efficient optimization method for the proposed objective function. Experimental results showed that our method on twelve data sets achieves the best performance compared with other similar models.

https://link.springer.com/content/pdf/10.1007/s11063-020-10383-9.pdf

Joint Spectral Clustering based on Optimal Graph and Feature Selection

The construction sector is a key driver of economic growth in New Zealand; however, its productivity is still considered to be low. Prior research has suggested that information and communication technology (ICT) can help enhance efficiency and productivity. However, there is little research on the use of mobile technologies by New Zealand construction workforce. This paper reports findings of an exploratory study with the objective of examining the perceived benefits regarding uptake of apps in New Zealand construction sector. Using self-administered questionnaire survey, feedback was received from the major construction trade and professional organisations in New Zealand. Survey data was analyzed using descriptive, one-sample t-test, Spearman’s rank correlation coefficient and structural equation modeling. Results showed that iPhone and Android phone currently dominate the smartphone market in New Zealand construction industry. The top three application areas are site photos, health and safety reporting and timekeeping. The benefits of mobile apps were widely confirmed by the construction professionals. The benefit of “better client relationship management and satisfaction” has substantial correlation with overall productivity improvement and best predictor of the overall productivity improvement. These findings provide a starting point for further research aimed at improving the uptake and full leveraging of mobile technologies to improve the dwindling productivity trend in New Zealand construction industry.

https://www.mdpi.com/2075-5309/7/4/111/pdf

The Perceived Benefits of Apps by Construction Professionals in New Zealand

Despite the computers have developed rapidly in recent years, there are still many difficulties to obtain a large number of labelled data in many practical problems, for example, medical image diagnosis, internet fraud, and pedestrian detection. To deal with learning problems with only a few labeled data, a novel semi-supervised learning method combined with dynamic graph learning with self-paced learning mechanism is present in this work, namely SS-GSELM. Firstly, according to the loss value of labeled samples in each training, the algorithm selects the sample with the smaller loss value for learning, and then gradually adds the sample with the larger loss value during the training process until all labeled samples are trained. In particular, different weights are given to samples through a regularization function to adjust the importance of different samples on the model results. Secondly, the algorithm uses local consistency property as supplementary information to enhance the performance of the learning machine, so an adaptive graph matrix is constructed to retain data similarity information. To do this, an alternative strategy is proposed to update graph matrices and self-paced weights to adapt to the classifier. Experimental results on real data sets exhibit that the proposed method superior to the classic methods in classification tasks.

Robust Adaptive Semi-supervised Classification Method based on Dynamic Graph and Self-paced Learning

This article describes how social politeness is relevant to computer system design. As the Internet becomes more social, computers now mediate social interactions, act as social agents and serve as information assistants. To succeed in these roles computers must learn a new skill politeness. Yet selfish software is currently a widespread problem, and politeness remains a software design “blind spot”. Using an informational definition of politeness, as the giving of social choice, suggests four aspects: 1. Respect, 2. Openness, 3. Helpfulness, and 4. Remembering. Examples are given to suggest how polite computing could make human-computer interactions more pleasant, and increase software usage. In contrast, if software rudeness makes the Internet an unpleasant place to be, usage may minimize. For the Internet to recognize its social potential, software must be not only useful and usable, but also polite.

Tong Liu

Papers

Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data

Joint Spectral Clustering based on Optimal Graph and Feature Selection

The Perceived Benefits of Apps by Construction Professionals in New Zealand

Robust Adaptive Semi-supervised Classification Method based on Dynamic Graph and Self-paced Learning

Politeness as a Social Computing Requirement