This article aims to analyze the views of two pioneering Ḥanafī scholars, Abū Manṣūr al- Māturīdī and Abū Zayd al-Dabūsī, on the meaning of death and life in terms of their general doctrine of religion. In the first part, the general framework of Māturīdī and Dabūsī’s evidentialist conception of religion are drawn. In the second part, Māturīdī's views on the meaning of death and life and are explored. In the third part, the views of Abū Zayd al-Dabūsī on the (...) meaning of death and life, who establishes a striking connection between the divine order and existence, divine prohibition and non-existence, are examined, and a general evaluation is offered in the last section. Al-Māturīdī proposed a very original and systematic example of doing philosophy of religion, which still promising for the modern problems of the field, by opening the phenomenon of religion itself to discussion, and analyzing the nature and elements of religion. Māturīdī aims to place the problems on a universal and theoretical basis and to present a holistic, metaphysical understanding while discussing specific and particular issues in religion. Just as there are main notes in classical Western music that repeat throughout the symphony in baroque style, there are universal principles in al-Māturīdī’s metaphysics that cut all particular debates vertically. The basic principle of the al-Māturīdī’s metaphysics is based on this idea: God is a wise being, he does not do anything wise. Accordingly, it is absurd to create something to destroy; God did not create man to perish. The love of life and the fear of death are the deepest existential problems facing man as a finite being that goes towards death. Religion can be regarded as a powerful alternative to other worldviews to the extent that it provides a reasonable explanation for man’s deep concern about finitude. For al-Māturīdī, neither pessimistic understandings that emphasize only the fear of death nor hedonistic understandings that ignore death offer a reasonable solution. God created death as something that people fear the most and avoid, and life as something they love and desire. The testing of man is about being between the fear of death and the desire to live in the world. According to al-Māturīdī, this dilemma that man is facing also shows him the way out. If people do good and useful deeds because of their love for life, and avoid nonsense and ugly deeds because of their afraid of death, they can make their life eternal with their own efforts and escape from eternal/permanent death. The other Hanafī thinker, a philosopher of law Abū Zayd al-Dabūsī’s views on the meaning of death and life are parallel to Māturīdī's. According to al-Dabūsī, to test man, what God has placed in human nature is not only the soul and reason but also the spirit (al-nafs) and whim (al-hawa). While the spirit and whim invites man to the temporary by ignorance, it calls the soul and reason to the permanent by knowledge. The test for human beings is to choose between the temporary and the eternal life. For al-Dabūsī, man’s saving himself from falling into nothingness and gaining the eternal life depends on his use of reason. In other words, the reason binds human beings to life, and the whim leads them to nothingness. Drawing a parallel between reasoning and existence, ignorance and absence, Dabūsī defines the terms divine command and divine prohibition in a very original way. For him, the divine command is the things that must exist, and the divine prohibition is the things that must not exist. If the man helps exist the things that God wills to exist, he can make his own existence eternal. If the man helps exist the things that God does not will to exist, he puts himself into nothingness. Lastly, both for al-Māturīdī and al-Dabūsī, life is a divine grace bestowed on human being to gain eternal life through the good deeds he does at any moment. In short, reason is necessary in religion because its use is a preference of existence to non-existence. This metaphysical system, which sees goodness as a bridge from this finite world to heaven and eternity and makes a connection between faith and deeds on the ontological and ethical level is the essence of the worldview of the Hanafī-Māturīdī tradition. (shrink)

Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able (...) to generate the rules necessary to correctly solve Raven's items, as well as recreate many of the experimental effects observed in human subjects. (shrink)

Neural machine translation (NMT) is one of the success stories of deep learning and artificial intelligence. Revolutionary innovations in the computational architectures made in 2015–2017 have led to dramatic improvements in the quality of machine translation (MT) and changed the field forever. Some professional translators welcome these changes with enthusiasm, others less so. But everyone has to deal with them. Historically, the relationship between human translation and MT has been uneasy and complicated, but an increasing number of players in (...) both fields are now coming to view it as synergistic. -/- OPUS-CAT is a collection of machine translation software based on a Windows implementation of Marian NMT—a stable production-ready neural machine translation framework used in many industrial and research applications. It can be installed on your PC, fine-tuned to specialized translation domains, and integrated into CAT tools. (shrink)

The ability to improve in speed and accuracy as a result of repeating some task is an important hallmark of intelligent biological systems. Although gradual behavioral improvements from practice have been modeled in spiking neural networks, few such models have attempted to explain cognitive development of a task as complex as addition. In this work, we model the progression from a counting-based strategy for addition to a recall-based strategy. The model consists of two networks working in parallel: a slower (...) basal ganglia loop and a faster cortical network. The slow network methodically computes the count from one digit given another, corresponding to the addition of two digits, whereas the fast network gradually “memorizes” the output from the slow network. The faster network eventually learns how to add the same digits that initially drove the behavior of the slower network. Performance of this model is demonstrated by simulating a fully spiking neural network that includes basal ganglia, thalamus, and various cortical areas. Consequently, the model incorporates various neuroanatomical data, in terms of brain areas used for calculation and makes psychologically testable predictions related to frequency of rehearsal. Furthermore, the model replicates developmental progression through addition strategies in terms of reaction times and accuracy, and naturally explains observed symptoms of dyscalculia. (shrink)

Many kinds of creativity result from combination of mental representations. This paper provides a computational account of how creative thinking can arise from combining neural patterns into ones that are potentially novel and useful. We defend the hypothesis that such combinations arise from mechanisms that bind together neural activity by a process of convolution, a mathematical operation that interweaves structures. We describe computer simulations that show the feasibility of using convolution to produce emergent patterns of neural activity (...) that can support cognitive and emotional processes underlying human creativity. (shrink)

We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms (...) is shown by a model that simulates psychologically important cases of intention. (shrink)

We present techniques for integrating low‐level neurobiological constraints into high‐level, functional cognitive models. In particular, we use these techniques to construct a model of eyeblink conditioning in the cerebellum based on temporal representations in the recurrent Granule‐Golgi microcircuit.

An increase in narcissism has been reported by experts over the years. Narcissists bring a lot of negative consequences to themselves and to the people around them. This study investigates that perceived parenting leads to the development of inflated, unstable self-concept. The inflated self-concept lays the framework for the development of narcissistic traits among individuals; perceived parenting affects this relationship. A sample of 628 adults was taken from North Cyprus through purposive sampling. Scales for perceived parenting, self-concept, and narcissism (...) were administered. Results showed that the statistically significant variance in the self-concept is explained by perceived parenting. Both mothers and fathers contributed significantly to the development of inflated self-concept. Moreover, multiple linear regression results showed a higher significant variance in narcissistic traits due to self-concept compared with perceived parenting. Mediational tests also showed that high levels of narcissistic traits were indirectly related to self-concept through perceived parenting. The study findings have challenged the notion of only mothers being responsible for narcissistic traits among their children. The study results also suggest that targeting self-concept in individuals with narcissistic traits may be a good directive for therapeutic interventions. (shrink)

This contribution aims at proposing a framework for articulating different kinds of “normativities” that are and can be attributed to “algorithmic systems.” The technical normativity manifests itself through the lineage of technical objects. The norm expresses a technical scheme’s becoming as it mutates through, but also resists, inventions. The genealogy of neural networks shall provide a powerful illustration of this dynamic by engaging with their concrete functioning as well as their unsuspected potentialities. The socio-technical normativity accounts for the (...) manners in which engineers, as actors folded into socio-technical networks, willingly or unwittingly, infuse technical objects with values materialized in the system. Surveillance systems’ design will serve here to instantiate the ongoing mediation through which algorithmic systems are endowed with specific capacities. The behavioral normativity is the normative activity, in which both organic and mechanical behaviors are actively participating, undoing the identification of machines with “norm following,” and organisms with “norminstitution”. This proposition productively accounts for the singularity of machine learning algorithms, explored here through the case of recommender systems. The paper will provide substantial discussions of the notions of “normative” by cutting across history and philosophy of science, legal, and critical theory, as well as “algorithmics,” and by confronting our studies led in engineering laboratories with critical algorithm studies. (shrink)

Sociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of (...) the mobile robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further, we showed that our system produces fewer personal-zone violations, causing less discomfort. (shrink)

We use a spiking neural network model of working memory capable of performing the spatial delayed response task to investigate two drugs that affect WM: guanfacine and phenylephrine. In this model, the loss of information over time results from changes in the spiking neural activity through recurrent connections. We reproduce the standard forgetting curve and then show that this curve changes in the presence of GFC and PHE, whose application is simulated by manipulating functional, neural, and biophysical (...) properties of the model. In particular, applying GFC causes increased activity in neurons that are sensitive to the information currently being remembered, while applying PHE leads to decreased activity in these same neurons. Interestingly, these differential effects emerge from network-level interactions because GFC and PHE affect all neurons equally. We compare our model to both electrophysiological data from neurons in monkey dorsolateral prefrontal cortex and to behavioral evidence from monkeys performing the DRT. (shrink)

The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables entailed by the graph conditional dependencies. In this article, we deviate from the common assumption of linear relationships in causal models by making use of neural autoregressive density estimators and use them to estimate causal effects within Pearl’s (...) do-calculus framework. Using synthetic data, we show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables and include confidence bands using the non-parametric bootstrap. We also explore scenarios that deviate from the ideal causal effect estimation setting such as poor data support or unobserved confounders. (shrink)

This Discussion Note calls for attention to the cultural practices of Neural Engineering as part of the life sciences as practices and technologies of manufacturing life. Through focusing on Disability, Ableism, and especially Technoableism within the field, I point out instances of onto-epistemological violence, which influence the likelihood of survival of disabled people individually and as a group. By drawing on Crip Technoscience, a method assemblage is introduced that allows to address these issues in an intersectional-kyriarchal understanding of (...) interlocking systems of privilege and oppression through generative critique and productive collaborative work. Criptical Neural Engineering is dedicated to disability justice and disability gain. It centers disabled people as epistemic activists and demands response-ability and accountability from non-disabled people, specifically engineers that want to build adaptive technologies with disabled people. (shrink)

Human decision making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a (...) neural network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. In order to substantiate our viewpoint, we discern and explain four basic neural network principles: (1) Association, (2) Compatibility (3) Retainment, and (4) Focus. These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor functions. They form the basis for our inclinations to associate and combine (unrelated) information, to prioritize information that is compatible with our present state (such as knowledge, opinions and expectations), to retain given information that sometimes could better be ignored, and to focus on dominant information while ignoring relevant information that is not directly activated. The supposed mechanisms are complementary and not mutually exclusive. For different cognitive biases they may all contribute in varying degrees to distortion of information. The present viewpoint not only complements the earlier three viewpoints, but also provides a unifying and binding framework for many cognitive bias phenomena. (shrink)

To have a fully integrated understanding of neurobiological systems, we must address two fundamental questions: 1. What do brains do (what is their function)? and 2. How do brains do whatever it is that they do (how is that function implemented)? I begin by arguing that these questions are necessarily inter-related. Thus, addressing one without consideration of an answer to the other, as is often done, is a mistake. I then describe what I take to be the best available approach (...) to addressing both questions. Specifically, to address 2, I adopt the Neural Engineering Framework (NEF) of Eliasmith & Anderson [Neural engineering: Computation representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press, 2003] which identifies implementational principles for neural models. To address 1, I suggest that adopting statistical modeling methods for perception and action will be functionally sufficient for capturing biological behavior. I show how these two answers will be mutually constraining, since the process of model selection for the statistical method in this approach can be informed by known anatomical and physiological properties of the brain, captured by the NEF. Similarly, the application of the NEF must be informed by functional hypotheses, captured by the statistical modeling approach. (shrink)

In this article, I discuss the concept of robustness in neuroscience. Various mechanisms for making systems robust have been discussed across biology and neuroscience. Many of these notions originate from engineering. I argue that concepts borrowed from engineering aid neuroscientists in operationalizing robustness, formulating hypotheses about mechanisms for robustness, and quantifying robustness. Furthermore, I argue that the significant disanalogies between brains and engineered artifacts raise important questions about the applicability of the engineering framework. I argue that (...) the use of such concepts should be understood as a kind of simplifying idealization. (shrink)

Juris-Data is one of the largest case-study base in France. The case studies are indexed by legal classification elaborated by the Juris-Data Group. Knowledge engineering was used to design an intelligent interface for information retrieval based on this classification. The aim of the system is to help users find the case-study which is the most relevant to their own.The approach is potentially very useful, but for standardising it for other legal document bases it is necessary to extract a legal (...) classification of the primary documents. Thus, a methodology for the construction of these classifications was designed together with a framework for index construction. The project led to the implementation of a Legal Case Studies Engineering Framework based on the accumulated experimentation and the methodologies designed. It consists of a set of computerised tools which support the life-cycle of the legal document from their processing by legal experts to their consultation by clients. (shrink)

To have a fully integrated understanding of neurobiological systems, we must address two fundamental questions: 1. What do brains do (what is their function)? and 2. How do brains do whatever it is that they do (how is that function implemented)? I begin by arguing that these questions are necessarily inter-related. Thus, addressing one without consideration of an answer to the other, as is often done, is a mistake. I then describe what I take to be the best available approach (...) to addressing both questions. Specifically, to address 2, I adopt the Neural Engineering Framework (NEF) of Eliasmith & Anderson [Neural engineering: Computation representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press, 2003] which identifies implementational principles for neural models. To address 1, I suggest that adopting statistical modeling methods for perception and action will be functionally sufficient for capturing biological behavior. I show how these two answers will be mutually constraining, since the process of model selection for the statistical method in this approach can be informed by known anatomical and physiological properties of the brain, captured by the NEF. Similarly, the application of the NEF must be informed by functional hypotheses, captured by the statistical modeling approach. (shrink)

Duggins et al. use a spiking neural network model of working memory to predict the reaction to two drugs known to affect working memory (guanfacine and phenylephrine). The model can explain data from moneys at the biophysical, neural, and behavioral levels.

It has been suggested that Marr took the three levels he famously identifies to be independent. In this paper, we argue that Marr's view is more nuanced. Specifically, we show that the view explicitly articulated in his work attempts to integrate the levels, and in doing so results in Marr attacking both reductionism and vagueness. The result is a perspective in which both high-level information-processing constraints and low-level implementational constraints play mutually reinforcing and constraining roles. We discuss our recent work (...) on Spaun—currently the world's largest functional brain model—that demonstrates the positive impact of this kind of unifying integration of Marr's levels. We argue that this kind of integration avoids his concerns with both reductionism and vagueness. In short, we suggest that the methods behind Spaun can be used to satisfy Marr's explicit interest in combining high-level functional and detailed mechanistic explanations. (shrink)

Neural engineering technologies such as implanted deep brain stimulators and brain-computer interfaces represent exciting and potentially transformative tools for improving human health and well-being. Yet their current use and future prospects raise a variety of ethical and philosophical concerns. Devices that alter brain function invite us to think deeply about a range of ethical concerns—identity, normality, authority, responsibility, privacy, and justice. If a device is stimulating my brain while I decide upon an action, am I still the author (...) of the action? Does a device make the interiority of my experience accessible to others? Will the device change the way I think of myself and others think of me? Such fundamental questions arise even when a device is designed for only a relatively circumscribed purpose, such as restoring functioning via a smart prosthetic. We are part of a National Science Foundation-funded Engineering Research Center tasked with investigating philosophical and social implications of neural engineering research and technologies. Neural devices already in clinical use, such as deep brain stimulators for Parkinson's disease or essential tremor, have spurred healthy debate about such implications. Devices currently under development—such as the BrainGate System of implanted brain sensors coupled to robotics in persons with paralysis, exoskeletons for augmented movement, transcranial do-it-yourself stimulators, closed-loop brain stimulating systems, or even brain-to-brain interfacing—promise to extend and deepen these debates. At our center, brain-computer interfaces are the principal focus of work. Even acknowledging that the clinical translation of neural devices and seamless integration by end users may still largely reside in the future, the potential these devices hold calls for careful early analysis. The launching of the Brain Research through Advancing Innovative Neurotechnologies Initiative in April 2013 provides further impetus for this work. (shrink)

A brain tumor is an abnormal mass or growth of a cell that leads to certain death, and this is still a challenging task in clinical practice. Early and correct diagnosis of this type of cancer is very important for the treatment process. For this reason, this study aimed to develop computer-aided systems for the diagnosis of brain tumors. In this research, we proposed three different end-to-end deep learning approaches for analyzing effects of local and deep features for brain MRI (...) images anomaly detection. The first proposed system is Directional Bit-Planes Deep Autoencoder which extracts and learns local and direction features. The DBP-DAE by decomposition of a local binary pattern into eight bit-planes extracts are directional and inherent local-structure features from the input image and learns robust feature for classification purposes. The second one is a Dilated Separable Residual Convolutional Network which extracts high and low-level features. The main advantage of this approach is that it is robust and shows stable results regardless to size of image database and to solve overfitting problems. To explore the effects of mixture of local and deep extracted feature on accuracy of classification of brain anomaly, a multibranch convolutional neural network approach is proposed. This approach is designed according to combination of DBP-DAE and DSRCN in an end-to-end manner. Extensive experiments conducted based on brain tumor in MRI image public access databases and achieves significant results compared to state-of-the-art algorithms. In addition, we discussed the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s. (shrink)

Animal models of human diseases are often used in biomedical research in place of human subjects. However, results obtained by animal models may fail to hold true for humans. One way of addressing this problem is to make animal models more similar to humans by placing human tissue into animal models, rendering them chimeric. Since technical and ethical limitations make neurological disorders difficult to study in humans, chimeric models with human neural tissue could help advance our understanding of neuropathophysiology. (...) In this article, we examine whether the introduction of human neural tissue and any consequent cognitive change is relevant to the way we ought to treat chimeras. We argue that changes in cognitive abilities are morally relevant to the extent that they increase the capacities that affect the moral status of any entity, including awareness, autonomy, and sociability. We posit that no being, regardless of species, should be treated in a way that is incommensurate with its moral status. Finally, we propose a framework that can be used to guide ethical assessment of research involving chimeras with advanced cognitive capacities. We advance this framework as a useful tool for bringing relevant considerations to the forefront for those considering the ethical merit of proposed chimeric research. In doing so, we examine concepts relevant to the question of how any entity may be treated, including moral status, dignity, and capacities. (shrink)

I present a case study of scientific discovery, where building two functional and behavioral approximations of neurons, one physical and the other computational, led to conceptual and implementation breakthroughs in a neural engineering laboratory. Such building of external systems that mimic target phenomena, and the use of these external systems to generate novel concepts and control structures, is a standard strategy in the new engineering sciences. I develop a model of the cognitive mechanism that connects such built (...) external systems with internal models, and I examine how new discoveries, and consensus on discoveries, could arise from this external‐internal coupling and the building process. The model is based on the emerging framework of common coding, which proposes a shared representation in the brain between the execution, perception, and imagination of movement. (shrink)

Genetically engineered animals that are meant for release in the wild could significantly impact ecosystems given the interwoven or entangled existence of species. Therefore, among other things, it is all too important that regulatory agencies conduct entity appropriate, rigorous risk assessments that can be used for informed decision-making at the local, national and global levels about the release of those animals in the wild. In the United States, certain GE animals that are intended for release in the wild may be (...) regulated as new animal drugs by the Food and Drug Administration. This paper argues that the decision to treat them as new animal drugs is attributable to the influence of neoliberalism on the US biotechnology regulatory policy framework. The case is made that there should be public democratic deliberations and decision-making about the values and concerns that should guide the nation’s biotechnology regulatory policy paradigm, including the risk assessment process for GE animals meant for release in the wild. -/- . (shrink)

Recent experiments have been used to “edit” genomes of various plant, animal and other species, including humans, with unprecedented precision. Furthermore, editing the Cas9 endonuclease gene with a gene encoding the desired guide RNA into an organism, adjacent to an altered gene, could create a “gene drive” that could spread a trait through an entire population of organisms. These experiments represent advances along a spectrum of technological abilities that genetic engineers have been working on since the advent of recombinant DNA (...) techniques. The scientific and bioethics communities have built substantial literatures about the ethical and policy implications of genetic engineering, especially in the age of bioterrorism. However, recent CRISPr/Cas experiments have triggered a rehashing of previous policy discussions, suggesting that the scientific community requires guidance on how to think about social responsibility. We propose a framework to enable analysis of social responsibility, using two example.. (shrink)

The term “synthetic biology” is a popular label of an emerging biotechnological field with strong claims to robustness, modularity, and controlled construction, finally enabling the creation of new organisms. Although the research community is heterogeneous, it advocates a common denominator that seems to define this field: the principles of rational engineering. However, it still remains unclear to what extent rational engineering—rather than “tinkering” or the usage of random based or non-rational processes—actually constitutes the basis for the techniques of (...) synthetic biology. In this article, we present the results of a quantitative bibliometric analysis of the realized extent of rational engineering in synthetic biology. In our analysis, we examine three issues: (1) We evaluate whether work at three levels of synthetic biology (parts, devices, and systems) is consistent with the principles of rational engineering. (2) We estimate the extent of rational engineering in synthetic biology laboratory practice by an evaluation of publications in synthetic biology. (3) We examine the methodological specialization in rational engineering of authors in synthetic biology. Our analysis demonstrates that rational engineering is prevalent in about half of the articles related to synthetic biology. Interestingly, in recent years the relative number of respective publications has decreased. Despite its prominent role among the claims of synthetic biology, rational engineering has not yet entirely replaced biotechnological methods based on “tinkering” and non-rational principles. (shrink)

A view is put forward, according to which various aspects of the structure of the world as internalized by the brain take the form of “neural spaces,” a concrete counterpart for Shepard's “abstract” ones. Neural spaces may help us understand better both the representational substrate of cognition and the processes that operate on it. [Shepard].

The widespread use of online search engines to answer the general public’s needs for information has raised concerns about possible biases and the emerging of a ‘filter bubble’ in which users are isolated from attitude-discordant messages. Research is split between approaches that largely focus on the intrinsic limitations of search engines and approaches that investigate user search behavior. This work evaluates the findings and limitations of both approaches and advances a theoretical framework for empirical investigations of cognitive biases in (...) online search activities about health-related topics. We aim to investigate the interaction between the user and the search engine as a whole. Online search activity about health-related topics is considered as a hypothesis-testing process. Two questions emerge: whether the retrieved information provided by the search engines are fit to fulfill their role as evidence, and whether the use of this information by users is cognitively and epistemologically valid and unbiased. (shrink)

Proposed ways of governing climate engineering have most often been supported by narrowly framed and unreflexive appraisals and processes. This article explores the governance implications of a Deliberative Mapping project that, unlike other governance principles, have emerged from an extensive process of reflection and reflexivity. In turn, the project has made significant advances in addressing the current deficit of responsibly defined criteria for shaping governance propositions. Three such propositions argue that reflexive foresight of the imagined futures in which climate (...) engineering proposals might reside is required; the performance and acceptance of climate engineering proposals should be decided in terms of robustness, not optimality; and climate engineering proposals should be satisfactorily opened up before they can be considered legitimate objects of governance. Taken together, these propositions offer a sociotechnical framework not simply for governing climate engineering but for governing responses to climate change at large. (shrink)

The construction industry is usually characterized as a fragmented system of multiple-organizational entities in which members from different technical backgrounds and moral values join together to develop a particular business or project. The greatest challenge in the construction process for the achievement of a successful practice is the development of an outstanding reputation, which is built on identifying and applying an ethical framework. This framework should reflect a common ethical ground for myriad people involved in this process to (...) survive and compete ethically in today’s turbulent construction market. This study establishes a framework for ethical judgment of behavior and actions conducted in the construction process. The framework was primarily developed based on the essential attributes of business management identified in the literature review and subsequently incorporates additional attributes identified to prevent breaches in the construction industry and common ethical values related to professional engineering. The proposed judgment framework is based primarily on the ethical dimension of professional responsibility. The Ethical Judgment Framework consists of descriptive approaches involving technical, professional, administrative, and miscellaneous terms. The framework provides the basis for judging actions as either ethical or unethical. Furthermore, the framework can be implemented as a form of preventive ethics, which would help avoid ethical dilemmas and moral allegations. The framework can be considered a decision-making model to guide actions and improve the ethical reasoning process that would help individuals think through possible implications and consequences of ethical dilemmas in the construction industry. (shrink)

The purpose of this article is to propose an ethical framework for software engineers that connects software developers’ ethical responsibilities directly to their professional standards. The implementation of such an ethical framework can overcome the traditional dichotomy between professional skills and ethical skills, which plagues the engineering professions, by proposing an approach to the fundamental tasks of the practitioner, i.e., software development, in which the professional standards are intrinsically connected to the ethical responsibilities. In so doing, the (...) ethical framework improves the practitioner’s professionalism and ethics. We call this approach Ethical-Driven Software Development, as an approach to software development. EDSD manifests the advantages of an ethical framework as an alternative to the all too familiar approach in professional ethics that advocates “stand-alone codes of ethics”. We believe that one outcome of this synergy between professional and ethical skills is simply better engineers. Moreover, since there are often different software solutions, which the engineer can provide to an issue at stake, the ethical framework provides a guiding principle, within the process of software development, that helps the engineer evaluate the advantages and disadvantages of different software solutions. It does not and cannot affect the end-product in and of-itself. However, it can and should, make the software engineer more conscious and aware of the ethical ramifications of certain engineering decisions within the process. (shrink)

Although undergraduate engineering ethics courses often include the development of moral sensitivity as a learning objective and the use of active learning techniques, teaching centers on the transmission of cognitive knowledge. This article describes a complementary assignment asking students to perform an ethics “experiment” on themselves that has a potential to enhance affective learning and moral imagination. The article argues that the focus on cognitive learning may not promote, and may even impair, our efforts to foster moral sensitivity. In (...) contrast, the active learning assignments and exercises, like the ethics “experiment” discussed, offer great potential to expand the scope of instruction in engineering ethics to include ethical behavior as well as knowledge. Engineering ethics education needs to extend beyond the narrow range of human action associated with the technical work of the engineer and explore ways to draw on broader lifeworld experiences to enrich professional practice and identity. (shrink)

Faculty in a large, urban school of engineering designed a longitudinal study to assess the critical thinking skills of undergraduate students as they progressed through the engineering program. The Paul-Elder critical thinking framework was used to design course assignments and develop a holistic assessment rubric. This paper presents the analysis of the freshman course artifacts (baseline and course critical thinking assignments) and associated faculty scoring sessions for all three cohorts. A total of 649 first semester freshman students (...) at least 18 years old agreed to participate in the study. The majority were white males with a mean high school grade point average of 3.73, ACT composite score of 28.33, and final freshman engineering course grade of 3.57. There was a statistically significant positive relationship between the freshman course artifacts and the faculty scores. Data from the study are being used to enhance the critical thinking experiences for undergraduate engineering students. (shrink)

Faculty in a large, urban school of engineering designed a longitudinal study to assess the critical thinking skills of undergraduate students as they progressed through the engineering program. The Paul-Elder critical thinking framework was used to design course assignments and develop a holistic assessment rubric. This paper presents the analysis of the freshman course artifacts (baseline and course critical thinking assignments) and associated faculty scoring sessions for all three cohorts. A total of 649 first semester freshman students (...) at least 18 years old agreed to participate in the study. The majority were white males with a mean high school grade point average of 3.73, ACT composite score of 28.33, and final freshman engineering course grade of 3.57. There was a statistically significant positive relationship between the freshman course artifacts and the faculty scores. Data from the study are being used to enhance the critical thinking experiences for undergraduate engineering students. (shrink)

The automated valuation of benchmark land price plays an essential role in regulating land demand in Chinese real-estate market as the big data are currently accumulated rapidly. However, this problem becomes highly challenging due to the multidimension, large volume, and nonlinearity of the land price-influencing factors. In this paper, an effective data-driven automated valuation framework is proposed for valuing real estate assets by combining a GIS and neural network technologies. This framework can automatically obtain the values of (...) spatial factors affecting land price from GIS and generate training set data for training the neural network to identify the complex relationship between all kinds of factors and benchmark land prices. The effectiveness and universality of the framework is verified via the data of benchmark land prices in Wuhan. The framework can be applied for automated benchmark land price valuation in other cities. (shrink)

The construction industry is typically characterized as a fragmented, multi-organizational setting in which members from different technical backgrounds and moral values join together to develop a particular business or project. The most challenging obstacle in the construction process is to achieve a successful practice and to identify and apply an ethical framework to manage the behavior of involved specialists and contractors and to ensure the quality of all completed construction activities. The framework should reflect a common moral ground (...) for myriad people involved in this process to survive and compete ethically in today’s turbulent construction market. This study establishes a framework for moral judgment of behavior and actions conducted in the construction process. The moral framework provides the basis of judging actions as “moral” or “immoral” based on three levels of moral accountability: personal, professional, and social. The social aspect of the proposed framework is developed primarily from the essential attributes of normative business decision-making models identified in the literature review and subsequently incorporates additional attributes related to professional and personal moral values. The normative decision-making models reviewed are based primarily on social attributes as related to moral theories. The professional and moral attributes are established by identifying a set of common moral values recognized by professionals in the construction industry and required to prevent common construction breaches. The moral framework presented here is the complementary part of the ethical framework developed in Part I of this article and is based primarily on the personal behavior or the moral aspect of professional responsibility. The framework can be implemented as a form of preventive personal ethics, which would help avoid ethical dilemmas and moral implications in the first place. Furthermore, the moral framework can be considered as a decision-making model to guide actions and improve the moral reasoning process, which would help individuals think through possible implications and the consequences of ethical and moral issues in the construction industry. (shrink)

Driving a car is a highly visual task. Despite the trend towards increased driver assistance and autonomous vehicles, drivers still need to interact with the car for both driving and non-driving relevant tasks, at times simultaneously. The often-resulting high cognitive load is a safety issue which can be addressed by providing the driver with alternative feedback modalities, such as haptics. Recent trends in the automotive industry are moving towards the seamless integration of control elements through touch-sensitive surfaces. Psychological knowledge on (...) optimally utilizing haptic technologies remains limited. The literature on automotive haptic feedback consists mainly of singular findings without putting them into a broader user context with respect to haptic design of interfaces. Moreover, haptic feedback has primarily been limited to the confirmation of control actions rather than the searching or finding of control elements, the latter of which becomes particularly important considering the current trends. This paper presents an integrated framework on haptic processing in automotive user interfaces and provides guidelines for haptic design of user interfaces in car interiors. (shrink)

In this paper a theoretical framework is proposed for how the brain processes the information necessary for us to achieve the understanding of others that we experience in our social worlds. Our framework attempts to expand several previous approaches to more fully account for the various data on interpersonal understanding and to respond to theoretical critiques in this area. Specifically, we propose that social understanding must be achieved by at least two mechanisms in the brain that are capable (...) of parallel information processing. The first mechanism, based on research into mirror matching systems in the brain, suggests that representations of others are mapped onto an observer's representations of these same schemas in order to understand them. The second mechanism requires semantic analysis of a given social situation in order to understand the actions of others and most likely involves conscious processes. We suggest that experimental correlates of these systems should be dissociable using both behavioral and neuroimaging techniques. (shrink)

This chapter contains sections titled: General Ethical Issues Cellular, Genetic and Tissue Engineering Biomaterials, Prostheses and Implants Biomedical Imaging and Optics Neural Engineering References and Further Reading.

Engineering ethics is a required aspect of accredited ABET programs, but there is widespread variation in how ethics is taught, to what ends, and how those ends are assessed. This variation makes it challenging to identify practices for teaching ethics to engineers aligned with extant practices in the field. In this study, we revise a recent coding framework by reviewing exemplary engineering ethics programs recognized by the National Academy of Engineering in 2016, or what we refer (...) to as “exemplars.” We pursue two primary objectives: (1) To apply and revise a prior coding framework to codify ethics learning objectives, instructional strategies, and assessment strategies in engineering education; and (2) To use the revised coding framework to identify trends in learning objectives, instructional strategies, and assessment strategies of NAE exemplars. We employ systemic review procedures to update the coding framework using 24 of 25 exemplars as a data source. The updated framework includes four primary categories associated with learning objectives, instructional strategies, assessment data collection strategies, and assessment design characteristics. Results indicate that ethical sensitivity or awareness was present in every exemplar as a learning objective, often alongside ethical reasoning-based learning objectives and the formation of professional skills. Exemplars employed numerous instructional strategies in tandem, as we coded eight out of 18 instructional strategies among at least half of the exemplars. Assignments/homework and summative reflections were the most oft-used sources of assessment data. Due to our challenges in coding assessment approaches, we offer practical suggestions for assessing engineering ethics instruction which are based on many of our coding discussions. We hope that this coding framework, the results classifying exemplary features of the NAE programs, and our practical suggestions can guide future instructors as they design, classify, assess, and report their approaches to engineering ethics education. (shrink)