Trying and the Arguments from Total Failure

New Volitionalism is a name for certain widespread conception of the nature of intentional action. Some of the standard arguments for New Volitionalism, the so-called arguments from total failure, have even acquired the status of basic assumptions for many other kinds of philosophers. It is therefore of singular interest to investigate some of the most important arguments from total failure. This is what I propose to do in this paper. My aim is not be to demonstrate that these arguments are inconsistent or that total failure and naked tryings are metaphysically impossible. Rather, my aim is be to build a case against the possibility of naked, independently existing tryings, by questioning how well we understand the scenarios invoked in their favour. Thus, rather than attempting to present a definitive metaphysical refutation of New Volitionalism, I attempt to diminish or demolish its underlying motivation.

Putting the system back into systems change: a framework for understanding and changing organizational and community systems

Systems change has emerged as a dominant frame through which local, state, and national funders and practitioners across a wide array of fields approach their work. In most of these efforts, change agents and scholars strive to shift human services and community systems to create better and more just outcomes and improve the status quo. Despite this, there is a dearth of frameworks that scholars, practitioners, and funders can draw upon to aid them in understanding, designing, and assessing this process from a systemic perspective. This paper provides one framework--grounded in systems thinking and change literatures--for understanding and identifying the fundamental system parts and interdependencies that can help to explain system functioning and leverage systems change. The proposed framework highlights the importance of attending to both the deep and apparent structures within a system as well as the interactions and interdependencies among these system parts. This includes attending to the dominant normative, resource, regulative, and operational characteristics that dictate the behavior and lived experiences of system members. The value of engaging critical stakeholders in problem definition, boundary construction, and systems analysis are also discussed. The implications of this framework for systems change researchers and practitioners are discussed.     

Kant, Nonaccidentalness and the Availability of Moral Worth

Contemporary Kantians who defend Kant's view of the superiority of the sense of duty as a form of motivation appeal to various ideas. Some say, if only implicitly, that the sense of duty is always ``available' to an agent, when she has a moral obligation. Some, like Barbara Herman, say that the sense of duty provides a ``nonaccidental' connection between an agent's motivation and the act's rightness. In this paper I show that the ``availability' and ``nonaccidentalness' arguments are in tension with one another. And the ``availability' idea, although certainly supported by some passages in Kant himself, is also clearly denied in other passages. My conclusion is that Kantians will need to abandon either availability or nonaccidentalness if they wish to have a consistent set of views about the sense of duty.     

Desire and the female analyst

The literature on erotic transference and countertransference between female analyst and male patient is reviewed and discussed. It is known that female analysts are less likely than their male colleagues to act out sexually with their patients. It has been claimed that a) male patients do not experience sustained erotic transferences, and b) female analysts do not experience erotic countertransferences with female or male patients. These views are challenged and it is argued that, if there is less sexual acting out by female analysts, it is not because of an absence of eros in the therapeutic relationship. The literature review covers material drawn from psychoanalysis, feminist psychotherapy, Jungian analysis, as well as some sociological and cultural sources. It is organized under the following headings: the gender of the analyst, sexual acting out, erotic transference, maternal and paternal transference, gender and power, counter-transference, incest taboo - mothers and sons and sexual themes in the transference.     

Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks

BackgroundThe Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.ResultsTraining four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.LimitationsThis study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.ConclusionsBased on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.     

Emotional labour strategies and job burnout: A meta-analysis of Chinese employees

Emotional labour strategies have a significant impact on job burnout. However, current research results are controversial and unclear. A meta-analysis was conducted to explore the relationship between emotional labour strategies (surface acting and deep acting) and job burnout. A total of 84 empirical studies that included 28,242 participants were selected. The meta-analysis of the relationships between job burnout and surface acting and deep acting included 84 and 75 independent samples respectively. The results show that surface acting had a positive association (r = 0.25) with job burnout, whereas deep acting (r = −0.27) had a negative association with job burnout. Occupational types and measures of emotional labour strategies moderated the relationship between emotional labour strategies and job burnout, but this relationship was not moderated by measures of job burnout.     

FNDNet – A deep convolutional neural network for fake news detection

With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.     

Implicit and explicit processing in deep dyslexia: Semantic blocking as a test for failure of inhibition in the phonological output lexicon

The failure of inhibition hypothesis posits a theoretical distinction between implicit and explicit access in deep dyslexia. Specifically, the effects of failure of inhibition are assumed only in conditions that have an explicit selection requirement in the context of production (i.e., aloud reading). In contrast, the failure of inhibition hypothesis proposes that implicit processing and explicit access to semantic information without production demands are intact in deep dyslexia. Evidence for intact implicit and explicit access requires that performance in deep dyslexia parallels that observed in neurologically intact participants on tasks based on implicit and explicit processes. In other words, deep dyslexics should produce normal effects in conditions with implicit task demands (i.e., lexical decision) and on tasks based on explicit access without production (i.e., forced choice semantic decisions) because failure of inhibition does not impact the availability of lexical information, only explicit retrieval in the context of production. This research examined the distinction between implicit and explicit processes in deep dyslexia using semantic blocking in lexical decision and forced choice semantic decisions as a test for the failure of inhibition hypothesis. The results of the semantic blocking paradigm support the distinction between implicit and explicit processing and provide evidence for failure of inhibition as an explanation for semantic errors in deep dyslexia.     

Proposal and evaluation of deep exploitation-oriented learning under multiple reward environment

Recently, deep reinforcement learning (DRL) has attracted considerable attention. The well-known deep Q-network (DQN) architecture successfully combines deep learning and Q-learning which is a representative reinforcement learning (RL) method. In general, RL and DRL require many trial-and-error searches. To overcome this limitation, alternative approaches called exploitation-oriented learning (XoL) and deep exploitation-oriented learning (DXoL) have been proposed.Although the effectiveness of DXoL for DQNs has been verified, its effectiveness in an environment where multiple types of rewards are present remains unclear. In this study, we apply the DXoL method to two applications with multiple reward types: the driver drowsiness determination system and the decision-making system. Our experimental results show that DXoL is more suitable for learning priorities among multiple rewards than DQNs in these applications.     

Neural Policy Style Transfer

Style Transfer has been proposed in a number of fields: fine arts, natural language processing, and fixed trajectories. We scale this concept up to control policies within a Deep Reinforcement Learning infrastructure. Each network is trained to maximize the expected reward, which typically encodes the goal of an action, and can be described as the content. The expressive power of deep neural networks enables encoding a secondary task, which can be described as the style. The Neural Policy Style Transfer (NPST)¹ algorithm is proposed to transfer the style of one policy to another, while maintaining the content of the latter. Different policies are defined via Deep Q-Network architectures. These models are trained using demonstrations through Inverse Reinforcement Learning. Two different sets of user demonstrations are performed, one for content and other for style. Different styles are encoded as defined by user demonstrations. The generated policy is the result of feeding a content policy and a style policy to the NPST algorithm. Experiments are performed in a catch-ball game inspired by the Deep Reinforcement Learning classical Atari games; and a real-world painting scenario with a full-sized humanoid robot, based on previous works of the authors. The implementation of three different Q-Network architectures (Shallow, Deep and Deep Recurrent Q-Network) to encode the policies within the NPST framework is proposed and the results obtained in the experiments with each of these architectures compared.